tensorflow/python/eager/benchmarks_test.py
# Copyright 2021 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
r"""Benchmarks for low-level eager execution primitives.
To run CPU benchmarks:
bazel run -c opt benchmarks_test -- --benchmark_filter=.
To run GPU benchmarks:
bazel run --config=cuda -c opt --copt="-mavx" benchmarks_test -- \
--benchmark_filter=.
To run a subset of benchmarks using --benchmarks flag.
--benchmarks: the list of benchmarks to run. The specified value is interpreted
as a regular expression and any benchmark whose name contains a partial match
to the regular expression is executed.
e.g. --benchmark_filter=".*matmul*." will run all matmul related benchmarks.
"""
import time
import numpy as np
from tensorflow.python import pywrap_tfe
from tensorflow.python.compat import compat as forward_compat
from tensorflow.python.eager import backprop # pylint: disable=unused-import
from tensorflow.python.eager import benchmarks_test_base
from tensorflow.python.eager import context
from tensorflow.python.eager import core
from tensorflow.python.eager import def_function
from tensorflow.python.eager import forwardprop
from tensorflow.python.eager import test
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.framework import sparse_tensor
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import tensor_spec
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import cond
from tensorflow.python.ops import embedding_ops
from tensorflow.python.ops import functional_ops
from tensorflow.python.ops import gen_array_ops
from tensorflow.python.ops import gen_math_ops
from tensorflow.python.ops import gradients
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import random_ops
from tensorflow.python.ops import resource_variable_ops
from tensorflow.python.ops.ragged import ragged_tensor
from tensorflow.python.util import nest
from tensorflow.python.util import tf_inspect
CPU = "/device:CPU:0"
GPU = "/device:GPU:0"
GLOBAL_TEST_VALUE = None
def c_tfe_py_fastpath_execute(a,
b,
transpose_a=False,
transpose_b=False,
name=None):
ctx = context.context()
assert ctx.executing_eagerly(
), "The prototype doesn't contain C code for graph construction"
try:
return pywrap_tfe.TFE_Py_FastPathExecute(ctx, "MatMul", name, a, b,
"transpose_a", transpose_a,
"transpose_b", transpose_b)
except core._NotOkStatusException as e:
if name is not None:
e.message += " name: " + name
raise core._status_to_exception(e) from None
def run_benchmark(func, num_iters, execution_mode=None):
ctx = context.context()
with context.execution_mode(execution_mode):
# call func to warm up
func()
if execution_mode == context.ASYNC:
ctx.executor.wait()
start = time.time()
for _ in range(num_iters):
func()
if execution_mode == context.ASYNC:
ctx.executor.wait()
end = time.time()
return end - start
class MicroBenchmarks(benchmarks_test_base.MicroBenchmarksBase):
def __init__(self):
# used for multiply benchmarks
self._m_2 = random_ops.random_uniform([2])
# used for matmul benchmarks
self._m_2_by_2 = random_ops.random_uniform((2, 2))
self._m_100_by_784 = random_ops.random_uniform((100, 784))
self._num_iters_2_by_2 = 30000
self._num_iters_100_by_784 = 30000
# used for conv2d benchmarks
self._m_8_28_28_3 = random_ops.random_uniform((8, 28, 28, 3))
self._m_1_3_3_1 = random_ops.random_uniform((1, 3, 3, 1))
# used for embedding benchmarks
self._m_10000_by_16 = random_ops.random_uniform((10000, 16))
def _get_benchmark_name(self):
"""Mostly copied from benchmark.py _get_name()."""
stack = tf_inspect.stack()
name = None
for frame in stack[::-1]:
f_locals = frame[0].f_locals
f_self = f_locals.get("self", None)
if isinstance(f_self, test.Benchmark):
name = frame[3] # Get the method name
# This is a hack to get around the fact that some methods might have a
# disable_tfrt decorator around them. In that case a function called
# 'decorated' wraps the real called function underneath and so we
# peek one deeper into the stack to get the real name.
if name == "decorated":
continue
else:
break
if name is None:
raise ValueError("Unable to determine calling Benchmark function.")
if context.is_tfrt_enabled():
name = name + "_tfrt"
return name
def _run(self, func, num_iters, execution_mode=None):
self.run_report(run_benchmark, func, num_iters, execution_mode)
def benchmark_create_np_array(self):
func = lambda: np.array([3.0])
self._run(func, 30000)
def _benchmark_create_tensor(self, value, dtype, device):
"""Benchmark overheads of creating a Tensor object."""
if device == GPU:
# Warmup the GPU
ops.EagerTensor(value, device=device)
def func():
ops.EagerTensor(value, device=device, dtype=dtype)
self._run(func, 30000)
def _benchmark_create_constant(self, value, dtype, cached=True):
global GLOBAL_TEST_VALUE
GLOBAL_TEST_VALUE = value
def cached_func():
constant_op.constant(value, dtype=dtype)
def uncached_func():
global GLOBAL_TEST_VALUE
GLOBAL_TEST_VALUE += 1
constant_op.constant(GLOBAL_TEST_VALUE, dtype=dtype)
func = cached_func if cached else uncached_func
with ops.device("GPU:0" if context.num_gpus() else "CPU:0"):
for _ in range(1000):
func() # Warmup.
self._run(func, 3000)
def benchmark_create_float_constant(self):
self._benchmark_create_constant(42.0, dtype=None)
def benchmark_create_float_constant_uncached(self):
self._benchmark_create_constant(42.0, dtype=None, cached=False)
def benchmark_create_int32_constant(self):
if context.num_gpus():
return # int32 constants are always allocated on CPU.
self._benchmark_create_constant(42, dtype=dtypes.int32)
def benchmark_create_int32_constant_uncached(self):
if context.num_gpus():
return # int32 constants are always allocated on CPU.
self._benchmark_create_constant(42, dtype=dtypes.int32, cached=False)
def _benchmark_add(self, a, b):
def func():
return memoryview(math_ops.add_v2(a, b))
with ops.device("GPU:0" if context.num_gpus() else "CPU:0"):
for _ in range(1000):
func() # Warmup.
self._run(func, 30000)
def _benchmark_add_operator_overload(self, a, b):
def func():
return memoryview(a + b)
with ops.device("GPU:0" if context.num_gpus() else "CPU:0"):
for _ in range(1000):
func() # Warmup.
self._run(func, 30000)
def benchmark_add_float_scalars(self):
self._benchmark_add(42.0, 24.0)
def benchmark_add_int32_scalars(self):
self._benchmark_add(42, 24)
def benchmark_add_float_scalar_tensor(self):
tensor_a = constant_op.constant(42.0)
tensor_b = constant_op.constant(24.0)
self._benchmark_add(tensor_a, tensor_b)
def benchmark_add_float_scalar_tensor_overloaded_operator(self):
tensor_a = constant_op.constant(42.0)
tensor_b = constant_op.constant(24.0)
self._benchmark_add_operator_overload(tensor_a, tensor_b)
def benchmark_add_int32_scalar_tensor(self):
tensor_a = constant_op.constant(42)
tensor_b = constant_op.constant(24)
self._benchmark_add(tensor_a, tensor_b)
def benchmark_add_float_dense_tensor(self):
tensor_a = constant_op.constant([[42.0, 42.0], [42.0, 42.0]])
tensor_b = constant_op.constant([[24.0, 24.0], [24.0, 24.0]])
self._benchmark_add(tensor_a, tensor_b)
def benchmark_add_int32_dense_tensor(self):
tensor_a = constant_op.constant([[42, 42], [42, 42]])
tensor_b = constant_op.constant([[24, 24], [24, 24]])
self._benchmark_add(tensor_a, tensor_b)
def benchmark_create_float_tensor_from_list_CPU(self):
self._benchmark_create_tensor([[3.0]], dtypes.float32.as_datatype_enum, CPU)
def benchmark_create_float_tensor_from_np_array_CPU(self):
self._benchmark_create_tensor(
np.array([[3.0]], dtype=np.float32), dtypes.float32.as_datatype_enum,
CPU)
def benchmark_create_int32_tensor_from_list_CPU(self):
self._benchmark_create_tensor([[3]], dtypes.int32.as_datatype_enum, CPU)
def benchmark_create_int32_tensor_from_np_array_CPU(self):
self._benchmark_create_tensor(
np.array([[3]], dtype=np.int32), dtypes.int32.as_datatype_enum, CPU)
def benchmark_create_float_tensor_from_list_GPU(self):
if not context.num_gpus():
return
self._benchmark_create_tensor([[3.0]], dtypes.float32.as_datatype_enum, GPU)
def benchmark_create_float_tensor_from_np_array_GPU(self):
if not context.num_gpus():
return
self._benchmark_create_tensor(
np.array([[3.0]], dtype=np.float32), dtypes.float32.as_datatype_enum,
GPU)
def benchmark_create_int32_tensor_from_list_GPU(self):
# int32's are kept on host memory even when executing on GPU.
if not context.num_gpus():
return
self._benchmark_create_tensor([[3]], dtypes.int32.as_datatype_enum, GPU)
def benchmark_create_int32_tensor_from_np_array_GPU(self):
# int32's are kept on host memory even when executing on GPU.
if not context.num_gpus():
return
self._benchmark_create_tensor(
np.array([[3]], dtype=np.int32), dtypes.int32.as_datatype_enum, GPU)
def benchmark_index_tensor_with_literal(self):
func = lambda: constant_op.constant([3.0])[0]
self._run(func, 30000)
def benchmark_index_tensor_with_tensor(self):
func = lambda idx=constant_op.constant(0): constant_op.constant([3.0])[idx]
self._run(func, 30000)
def benchmark_index_tensor_with_np_array(self):
func = lambda idx=np.array(0): constant_op.constant([3.0])[idx]
self._run(func, 30000)
def _benchmark_np_multiply(self, m, num_iters):
a = m.cpu().numpy()
func = lambda: a * a
self._run(func, num_iters)
def _benchmark_tf_multiply(self, m, num_iters):
func = lambda: m * m
self._run(func, num_iters)
def _benchmark_tf_conv2d(self, m1, m2, num_iters):
func = lambda: nn_ops.conv2d(m1, m2, strides=[1, 1, 1, 1], padding="VALID")
self._run(func, num_iters)
def _benchmark_tf_multiply_op(self, m, num_iters):
func = lambda: math_ops.multiply(m, m)
self._run(func, num_iters)
def benchmark_np_multiply(self):
self._benchmark_np_multiply(self._m_2, 30000)
def benchmark_tf_multiply_CPU(self):
with context.device(CPU):
m = self._m_2.cpu()
self._benchmark_tf_multiply(m, 30000)
def benchmark_tf_multiply_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2.gpu()
self._benchmark_tf_multiply(m, 30000)
def benchmark_tf_multiply_op_CPU(self):
with context.device(CPU):
m = self._m_2.cpu()
self._benchmark_tf_multiply_op(m, 30000)
def benchmark_tf_multiply_op_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2.gpu()
self._benchmark_tf_multiply_op(m, 30000)
def benchmark_tf_conv2d_CPU(self):
with context.device(CPU):
m1 = self._m_8_28_28_3.cpu()
m2 = self._m_1_3_3_1.cpu()
self._benchmark_tf_conv2d(m1, m2, 30000)
def benchmark_tf_conv2d_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m1 = self._m_8_28_28_3.gpu()
m2 = self._m_1_3_3_1.gpu()
self._benchmark_tf_conv2d(m1, m2, 30000)
def benchmark_tf_identity(self):
m = self._m_2
self._run(lambda: gen_array_ops.identity(m), 30000)
def benchmark_slowpath_tf_identity(self):
self._run(lambda: gen_array_ops.identity(1), 30000)
def benchmark_tfe_py_execute_identity(self):
m = self._m_2
ctx_handle = context.context()._handle
attrs = ("T", self._m_2.dtype.as_datatype_enum)
inputs = [m]
def f():
pywrap_tfe.TFE_Py_Execute(ctx_handle, None, "Identity", inputs, attrs, 1)
self._run(f, 30000)
def benchmark_tf_gradient_function_identity(self):
with context.device(CPU):
m = gen_array_ops.identity(self._m_2)
self._run(
lambda: backprop.gradients_function(gen_array_ops.identity, [0])(m),
30000)
def benchmark_tf_gradient_forward_identity(self):
with backprop.GradientTape() as tape:
m = self._m_2
tape.watch(m)
self._run(lambda: gen_array_ops.identity(m), 30000)
def benchmark_tf_gradient_tape_push_pop(self):
def f():
with backprop.GradientTape():
pass
self._run(f, 30000)
def benchmark_tf_gradient_function_no_op(self):
with context.device(CPU):
m = gen_array_ops.identity(self._m_2)
self._run(lambda: backprop.gradients_function(lambda x: x, [0])(m), 30000)
def _benchmark_np_matmul(self, m, transpose_b, num_iters):
a = m.cpu().numpy()
b = a.T if transpose_b else a
func = lambda: np.dot(a, b)
self._run(func, num_iters)
def _benchmark_tf_matmul(self,
m,
transpose_b,
num_iters,
execution_mode=None):
func = lambda: math_ops.matmul(m, m, transpose_b=transpose_b)
self._run(func, num_iters, execution_mode=execution_mode)
def _benchmark_gen_math_ops_matmul(self, m, transpose_b, num_iters):
def func():
gen_math_ops.mat_mul(m, m, transpose_b=transpose_b)
self._run(func, num_iters)
def _benchmark_tfe_py_fastpath_execute_matmul(self, m, transpose_b,
num_iters):
def func():
c_tfe_py_fastpath_execute(m, m, transpose_b=transpose_b)
self._run(func, num_iters)
def _benchmark_tfe_py_execute_matmul(self, m, transpose_b, num_iters):
inputs = [m, m]
# pylint: disable=protected-access
ctx_handle = context.context()._handle
# pylint: enable=protected-access
device = context.context().device_name
attrs = ("transpose_a", False, "transpose_b", transpose_b, "T",
m.dtype.as_datatype_enum)
def func():
pywrap_tfe.TFE_Py_Execute(ctx_handle, device, "MatMul", inputs, attrs, 1)
self._run(func, num_iters)
def _benchmark_defun_matmul(self,
m,
transpose_b,
num_iters,
execution_mode=None):
f = def_function.function(math_ops.matmul)
func = lambda: f(m, m, transpose_b=transpose_b)
self._run(func, num_iters, execution_mode=execution_mode)
def _benchmark_defun_matmul_with_signature(self,
m,
num_iters,
execution_mode=None):
@def_function.function(
input_signature=[tensor_spec.TensorSpec([2, 2], dtypes.float32)])
def defun_matmul(m):
return math_ops.matmul(m, m)
func = lambda: defun_matmul(m)
self._run(func, num_iters, execution_mode=execution_mode)
def _benchmark_defun_matmul_relaxed_shape(self,
m,
num_iters,
execution_mode=None):
@def_function.function(reduce_retracing=True)
def defun_matmul(m):
return math_ops.matmul(m, m)
m_3_by_3 = random_ops.random_uniform((3, 3))
defun_matmul(m_3_by_3)
func = lambda: defun_matmul(m)
self._run(func, num_iters, execution_mode=execution_mode)
def _benchmark_defun_args_matmul(self, m, num_iters, execution_mode=None):
@def_function.function
def defun_matmul(m):
return math_ops.matmul(m, m)
func = lambda: defun_matmul(m)
self._run(func, num_iters, execution_mode=execution_mode)
def _benchmark_nested_defun_matmul(self, m, transpose_b, num_iters):
inner = def_function.function(math_ops.matmul)
@def_function.function
def outer(a, b, c, transpose_b):
return math_ops.matmul(inner(a, b, transpose_b=transpose_b), c)
func = lambda: outer(m, m, m, transpose_b=transpose_b)
# Warmup before benchmark
for _ in range(1000):
func()
self._run(func, num_iters)
def _benchmark_defun_matmul_forward_backward(self,
m,
transpose_b,
num_iters,
execution_mode=None):
f = def_function.function(math_ops.matmul)
def func():
with backprop.GradientTape() as gt:
gt.watch(m)
y = f(m, m, transpose_b=transpose_b)
_ = gt.gradient(y, m)
self._run(func, num_iters, execution_mode=execution_mode)
def _benchmark_read_variable(self, m, num_iters):
self._run(m.value, num_iters)
def _benchmark_matmul_read_variable(self, m, num_iters):
self._benchmark_gen_math_ops_matmul(
m, transpose_b=False, num_iters=num_iters)
def _benchmark_matmul_read_variable_with_tape(self, m, num_iters):
with backprop.GradientTape() as tape:
tape.watch(m)
self._benchmark_gen_math_ops_matmul(
m, transpose_b=False, num_iters=num_iters)
def _benchmark_read_variable_with_tape(self, m, num_iters):
with backprop.GradientTape() as tape:
tape.watch(m)
self._run(m.value, num_iters)
# Benchmarks for A^2, A of dimension 2 by 2.
def benchmark_np_matmul_2_by_2(self):
self._benchmark_np_matmul(
self._m_2_by_2, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_tf_matmul_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_tf_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_tf_matmul_2_by_2_CPU_async(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_tf_matmul(
m,
transpose_b=False,
num_iters=self._num_iters_2_by_2,
execution_mode=context.ASYNC)
def benchmark_gen_math_ops_matmul_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_gen_math_ops_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_tfe_py_fastpath_execute_matmul_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_tfe_py_fastpath_execute_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_tfe_py_execute_matmul_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_tfe_py_execute_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_with_signature_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_matmul_with_signature(
m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_relaxed_shape_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_matmul_relaxed_shape(
m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_args_matmul_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_args_matmul(m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_CPU_async(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_matmul(
m,
transpose_b=False,
num_iters=self._num_iters_2_by_2,
execution_mode=context.ASYNC)
def _benchmark_matmul_forward_backward_2_by_2_CPU(self, run_eager=False):
def_function.run_functions_eagerly(run_eager)
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_matmul_forward_backward(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def_function.run_functions_eagerly(False)
def _benchmark_matmul_forward_backward_2_by_2_CPU_async(
self, run_eager=False):
def_function.run_functions_eagerly(run_eager)
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_defun_matmul_forward_backward(
m,
transpose_b=False,
num_iters=self._num_iters_2_by_2,
execution_mode=context.ASYNC)
def benchmark_defun_matmul_forward_backward_2_by_2_CPU(self):
self._benchmark_matmul_forward_backward_2_by_2_CPU(False)
def benchmark_defun_matmul_forward_backward_2_by_2_CPU_async(self):
self._benchmark_matmul_forward_backward_2_by_2_CPU_async(False)
def benchmark_defun_eager_matmul_forward_backward_2_by_2_CPU(self):
self._benchmark_matmul_forward_backward_2_by_2_CPU(True)
def benchmark_defun_eager_matmul_forward_backward_2_by_2_CPU_async(self):
self._benchmark_matmul_forward_backward_2_by_2_CPU_async(True)
def benchmark_tf_matmul_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_tf_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_tf_matmul_2_by_2_GPU_async(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_tf_matmul(
m,
transpose_b=False,
num_iters=self._num_iters_2_by_2,
execution_mode=context.ASYNC)
def benchmark_gen_math_ops_matmul_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_gen_math_ops_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_tfe_py_execute_matmul_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_tfe_py_execute_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_defun_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_with_signature_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_defun_matmul_with_signature(
m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_relaxed_shape_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_defun_matmul_relaxed_shape(
m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_args_matmul_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_defun_args_matmul(m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_matmul_2_by_2_GPU_async(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_defun_matmul(
m,
transpose_b=False,
num_iters=self._num_iters_2_by_2,
execution_mode=context.ASYNC)
def benchmark_nested_defun_matmul_2_by_2(self):
m = self._m_2_by_2.cpu()
self._benchmark_nested_defun_matmul(
m, transpose_b=False, num_iters=self._num_iters_2_by_2)
# Benchmarks for AA.T, A of dimension 100 by 784.
def benchmark_np_matmul_100_by_784(self):
self._benchmark_np_matmul(
self._m_100_by_784,
transpose_b=True,
num_iters=self._num_iters_100_by_784)
def benchmark_tf_matmul_100_by_784_CPU(self):
with context.device(CPU):
m = self._m_100_by_784.cpu()
self._benchmark_tf_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_tf_matmul_100_by_784_CPU_async(self):
with context.device(CPU):
m = self._m_100_by_784.cpu()
self._benchmark_tf_matmul(
m,
transpose_b=True,
num_iters=self._num_iters_100_by_784,
execution_mode=context.ASYNC)
def benchmark_gen_math_ops_matmul_100_by_784_CPU(self):
with context.device(CPU):
m = self._m_100_by_784.cpu()
self._benchmark_gen_math_ops_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_tfe_py_fastpath_execute_matmul_100_by_784_CPU(self):
with context.device(CPU):
m = self._m_100_by_784.cpu()
self._benchmark_tfe_py_fastpath_execute_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_tfe_py_execute_matmul_100_by_784_CPU(self):
with context.device(CPU):
m = self._m_100_by_784.cpu()
self._benchmark_tfe_py_execute_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_defun_matmul_100_by_784_CPU(self):
with context.device(CPU):
m = self._m_100_by_784.cpu()
self._benchmark_defun_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_tf_matmul_100_by_784_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_100_by_784.gpu()
self._benchmark_tf_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_tf_matmul_100_by_784_GPU_async(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_100_by_784.gpu()
self._benchmark_tf_matmul(
m,
transpose_b=True,
num_iters=self._num_iters_100_by_784,
execution_mode=context.ASYNC)
def benchmark_gen_math_ops_matmul_100_by_784_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_100_by_784.gpu()
self._benchmark_gen_math_ops_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_tfe_py_execute_matmul_100_by_784_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_100_by_784.gpu()
self._benchmark_tfe_py_execute_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def benchmark_defun_matmul_100_by_784_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = self._m_100_by_784.gpu()
self._benchmark_defun_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
@test_util.disable_tfrt(
"b/169371527: Support inserting transfer op in lowering.")
def benchmark_nested_defun_matmul_100_by_784_GPU(self):
m = self._m_100_by_784.gpu()
self._benchmark_nested_defun_matmul(
m, transpose_b=True, num_iters=self._num_iters_100_by_784)
def _benchmark_forwardprop_matmul_CPU(self, shape):
with ops.device(CPU):
m = random_ops.random_uniform(shape).cpu()
tangent = random_ops.random_uniform(shape).cpu()
def func():
with forwardprop.ForwardAccumulator(m, tangent) as acc:
result = math_ops.matmul(m, m, transpose_b=True)
return result, acc.jvp(result)
# Warmup before benchmark
for _ in range(100):
func()
self._run(func, 3000)
def _benchmark_forwardprop_in_defun_matmul_CPU(self, shape):
with ops.device(CPU):
@def_function.function
def compiled_function(x, tangent):
with forwardprop.ForwardAccumulator(x, tangent) as acc:
result = math_ops.matmul(x, x, transpose_b=True)
return result, acc.jvp(result)
m = random_ops.random_uniform(shape).cpu()
tangent = random_ops.random_uniform(shape).cpu()
func = lambda: compiled_function(m, tangent)
# Warmup before benchmark
for _ in range(100):
func()
self._run(func, 3000)
def _benchmark_forwardprop_in_defun_of_defun_matmul_CPU(self, shape):
with ops.device(CPU):
matmul = def_function.function(math_ops.matmul)
@def_function.function()
def compiled_function(x, tangent):
with forwardprop.ForwardAccumulator(x, tangent) as acc:
result = matmul(x, x, transpose_b=True)
return result, acc.jvp(result)
m = random_ops.random_uniform(shape).cpu()
tangent = random_ops.random_uniform(shape).cpu()
func = lambda: compiled_function(m, tangent)
# Warmup before benchmark
for _ in range(100):
func()
self._run(func, 3000)
def _benchmark_forwardprop_of_defun_matmul_CPU(self, shape):
with ops.device(CPU):
m = random_ops.random_uniform(shape).cpu()
tangent = random_ops.random_uniform(shape).cpu()
matmul = def_function.function(math_ops.matmul)
def func():
with forwardprop.ForwardAccumulator(m, tangent) as acc:
result = matmul(m, m, transpose_b=True)
return result, acc.jvp(result)
# Warmup before benchmark
for _ in range(100):
func()
self._run(func, 3000)
def benchmark_forwardprop_matmul_256_by_2096_CPU(self):
self._benchmark_forwardprop_matmul_CPU(shape=(256, 2096))
def benchmark_forwardprop_in_defun_matmul_256_by_2096_CPU(self):
self._benchmark_forwardprop_in_defun_matmul_CPU(shape=(256, 2096))
def benchmark_forwardprop_in_defun_of_defun_matmul_256_by_2096_CPU(self):
self._benchmark_forwardprop_in_defun_of_defun_matmul_CPU(shape=(256, 2096))
def benchmark_forwardprop_of_defun_matmul_256_by_2096_CPU(self):
self._benchmark_forwardprop_of_defun_matmul_CPU(shape=(256, 2096))
def benchmark_forwardprop_matmul_100_by_784_CPU(self):
self._benchmark_forwardprop_matmul_CPU(shape=(100, 784))
def benchmark_forwardprop_in_defun_matmul_100_by_784_CPU(self):
self._benchmark_forwardprop_in_defun_matmul_CPU(shape=(100, 784))
def benchmark_forwardprop_in_defun_of_defun_matmul_100_by_784_CPU(self):
self._benchmark_forwardprop_in_defun_of_defun_matmul_CPU(shape=(100, 784))
def benchmark_forwardprop_of_defun_matmul_100_by_784_CPU(self):
self._benchmark_forwardprop_of_defun_matmul_CPU(shape=(100, 784))
def _benchmark_tf_reduce_logsumexp(self,
device=CPU,
execution_mode=None,
defunc=False,
xla_compile=False):
with context.device(device):
x = constant_op.constant([[1, 0.], [0., 0.]])
if defunc:
reduce_func = def_function.function(
math_ops.reduce_logsumexp, jit_compile=xla_compile)
func = lambda: reduce_func(x)
else:
func = lambda: math_ops.reduce_logsumexp(x)
self._run(func, 3000, execution_mode=execution_mode)
def benchmark_tf_reduce_logsumexp_CPU(self):
self._benchmark_tf_reduce_logsumexp()
def benchmark_tf_reduce_logsumexp_CPU_async(self):
self._benchmark_tf_reduce_logsumexp(execution_mode=context.ASYNC)
def benchmark_tf_reduce_logsumexp_GPU(self):
self._benchmark_tf_reduce_logsumexp(device=GPU)
def benchmark_tf_reduce_logsumexp_GPU_async(self):
self._benchmark_tf_reduce_logsumexp(
device=GPU, execution_mode=context.ASYNC)
@test_util.disable_tfrt(
"b/169371527: Support inserting transfer op in lowering.")
def benchmark_tf_reduce_logsumexp_CPU_defunc(self):
self._benchmark_tf_reduce_logsumexp(defunc=True)
@test_util.disable_tfrt(
"b/169371527: Support inserting transfer op in lowering.")
def benchmark_tf_reduce_logsumexp_CPU_async_defun(self):
self._benchmark_tf_reduce_logsumexp(
execution_mode=context.ASYNC, defunc=True)
def benchmark_tf_reduce_logsumexp_GPU_defun(self):
self._benchmark_tf_reduce_logsumexp(device=GPU, defunc=True)
def benchmark_tf_reduce_logsumexp_GPU_async_defun(self):
self._benchmark_tf_reduce_logsumexp(
device=GPU, execution_mode=context.ASYNC, defunc=True)
def benchmark_tf_reduce_logsumexp_GPU_defun_compile(self):
self._benchmark_tf_reduce_logsumexp(
device=GPU, defunc=True, xla_compile=True)
def benchmark_tf_reduce_logsumexp_GPU_async_defun_compile(self):
self._benchmark_tf_reduce_logsumexp(
device=GPU, execution_mode=context.ASYNC, defunc=True, xla_compile=True)
def _benchmark_tf_tensordot(self, device=CPU, execution_mode=None):
with context.device(device):
a = array_ops.ones((2, 2))
b = array_ops.ones((2, 2))
func = lambda: math_ops.tensordot(a, b, [[1], [0]])
self._run(func, 30000, execution_mode=execution_mode)
def benchmark_tf_tensordot_CPU(self):
self._benchmark_tf_tensordot()
def benchmark_tf_tensordot_CPU_async(self):
self._benchmark_tf_tensordot(execution_mode=context.ASYNC)
def benchmark_tf_tensordot_GPU(self):
self._benchmark_tf_tensordot(device=GPU)
def benchmark_tf_tensordot_GPU_async(self):
self._benchmark_tf_tensordot(device=GPU, execution_mode=context.ASYNC)
def _benchmark_tf_zeros(self, shape, dtype, device=CPU):
with context.device(device):
func = lambda: array_ops.zeros(shape, dtype)
self._run(func, 3000)
def benchmark_tf_zeros_2_by_2_float32_CPU(self):
self._benchmark_tf_zeros((2, 2), dtypes.float32)
def benchmark_tf_zeros_2_by_2_bool_CPU(self):
self._benchmark_tf_zeros((2, 2), dtypes.bool)
def benchmark_tf_zeros_2_by_2_string_CPU(self):
self._benchmark_tf_zeros((2, 2), dtypes.string)
def benchmark_tf_zeros_2_by_2_float32_GPU(self):
self._benchmark_tf_zeros((2, 2), dtypes.float32, device=GPU)
def benchmark_tf_zeros_2_by_2_bool_GPU(self):
self._benchmark_tf_zeros((2, 2), dtypes.bool, device=GPU)
def benchmark_tf_zeros_30_by_30_float32_CPU(self):
self._benchmark_tf_zeros((30, 30), dtypes.float32)
def benchmark_tf_zeros_30_by_30_bool_CPU(self):
self._benchmark_tf_zeros((30, 30), dtypes.bool)
def benchmark_tf_zeros_30_by_30_string_CPU(self):
self._benchmark_tf_zeros((30, 30), dtypes.string)
def benchmark_tf_zeros_30_by_30_float32_GPU(self):
self._benchmark_tf_zeros((30, 30), dtypes.float32, device=GPU)
def benchmark_tf_zeros_30_by_30_bool_GPU(self):
self._benchmark_tf_zeros((30, 30), dtypes.bool, device=GPU)
def benchmark_tf_zeros_100_by_100_float32_CPU(self):
self._benchmark_tf_zeros((100, 100), dtypes.float32)
def benchmark_tf_zeros_100_by_100_bool_CPU(self):
self._benchmark_tf_zeros((100, 100), dtypes.bool)
def benchmark_tf_zeros_100_by_100_string_CPU(self):
self._benchmark_tf_zeros((100, 100), dtypes.string)
def benchmark_tf_zeros_100_by_100_float32_GPU(self):
self._benchmark_tf_zeros((100, 100), dtypes.float32, device=GPU)
def benchmark_tf_zeros_100_by_100_bool_GPU(self):
self._benchmark_tf_zeros((100, 100), dtypes.bool, device=GPU)
def _benchmark_tf_zeros_like(self, m, device=CPU):
with context.device(device):
func = lambda: array_ops.zeros_like(m)
self._run(func, 3000)
def benchmark_tf_zeros_like_CPU(self):
self._benchmark_tf_zeros_like(self._m_2_by_2)
def benchmark_tf_zeros_like_GPU(self):
self._benchmark_tf_zeros_like(self._m_2_by_2, device=GPU)
def benchmark_tf_zeros_like_variable_CPU(self):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_tf_zeros_like(m)
def benchmark_tf_zeros_like_variable_GPU(self):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_tf_zeros_like(m, device=GPU)
def _benchmark_tf_random_uniform_2_by_2(self,
shape=(2, 2),
dtype=dtypes.int32,
device=CPU):
with context.device(device):
def func():
return random_ops.random_uniform(shape, maxval=3, dtype=dtype)
self._run(func, num_iters=self._num_iters_2_by_2)
def benchmark_tf_random_uniform_2_by_2_integer_CPU(self):
self._benchmark_tf_random_uniform_2_by_2()
def benchmark_tf_random_uniform_2_by_2_integer_GPU(self):
self._benchmark_tf_random_uniform_2_by_2(device=GPU)
def benchmark_tf_random_uniform_2_by_2_float_CPU(self):
self._benchmark_tf_random_uniform_2_by_2(dtype=dtypes.float32)
def benchmark_tf_random_uniform_2_by_2_float_GPU(self):
self._benchmark_tf_random_uniform_2_by_2(dtype=dtypes.float32, device=GPU)
def benchmark_tf_random_uniform_2_by_2_default_setting_CPU(self):
with context.device(CPU):
func = lambda: random_ops.random_uniform((2, 2))
self._run(func, num_iters=self._num_iters_2_by_2)
def benchmark_tf_random_uniform_2_by_2_default_setting_GPU(self):
with context.device(GPU):
func = lambda: random_ops.random_uniform((2, 2))
self._run(func, num_iters=self._num_iters_2_by_2)
def _benchmark_tf_dropout_2_by_2(self,
rate=0.5,
is_rate_tensor=True,
noise_shape=None,
device=CPU):
if is_rate_tensor:
rate = constant_op.constant(rate, dtype=dtypes.float32)
with context.device(device):
def func():
return nn_ops.dropout(
self._m_2_by_2, rate=rate, noise_shape=noise_shape)
self._run(func, num_iters=self._num_iters_2_by_2)
def benchmark_tf_dropout_scalar_rate_2_by_2_CPU(self):
self._benchmark_tf_dropout_2_by_2(is_rate_tensor=False)
def benchmark_tf_dropout_scalar_rate_2_by_2_GPU(self):
self._benchmark_tf_dropout_2_by_2(is_rate_tensor=False, device=GPU)
def benchmark_tf_dropout_2_by_2_CPU(self):
self._benchmark_tf_dropout_2_by_2()
def benchmark_tf_dropout_2_by_2_GPU(self):
self._benchmark_tf_dropout_2_by_2(device=GPU)
def benchmark_tf_dropout_scalar_rate_2_by_2_CPU_rate_0(self):
self._benchmark_tf_dropout_2_by_2(rate=0, is_rate_tensor=False)
def benchmark_tf_dropout_scalar_rate_2_by_2_GPU_rate_0(self):
self._benchmark_tf_dropout_2_by_2(
rate=0.0, is_rate_tensor=False, device=GPU)
def benchmark_tf_dropout_2_by_2_CPU_rate_0(self):
self._benchmark_tf_dropout_2_by_2(rate=0.0)
def benchmark_tf_dropout_2_by_2_GPU_rate_0(self):
self._benchmark_tf_dropout_2_by_2(rate=0, device=GPU)
def _benchmark_transpose(self,
m,
num_iters,
perm=None,
conjugate=False,
execution_mode=None):
func = lambda: array_ops.transpose(m, perm, conjugate)
self._run(func, num_iters, execution_mode=execution_mode)
def benchmark_tf_transpose_2_by_2_CPU(self):
with context.device(CPU):
m = self._m_2_by_2.cpu()
self._benchmark_transpose(m, num_iters=self._num_iters_2_by_2)
def benchmark_tf_transpose_2_by_2_GPU(self):
with context.device(GPU):
m = self._m_2_by_2.gpu()
self._benchmark_transpose(m, num_iters=self._num_iters_2_by_2)
def benchmark_tf_transpose_variable_2_by_2_CPU(self):
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_transpose(m, num_iters=self._num_iters_2_by_2)
def benchmark_tf_transpose_variable_2_by_2_GPU(self):
with context.device(GPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_transpose(m, num_iters=self._num_iters_2_by_2)
def benchmark_defun_without_signature(self):
def func(t1, t2, t3, t4, t5, t6, t7, t8):
del t1, t2, t3, t4, t5, t6, t7, t8
return None
defined = def_function.function(func)
t = constant_op.constant(0.0)
cache_computation = lambda: defined(t, t, t, t, t, t, t, t)
self._run(cache_computation, 30000)
def benchmark_defun_without_signature_and_with_kwargs(self):
def func(t1, t2, t3, t4, t5, t6, t7, t8):
del t1, t2, t3, t4, t5, t6, t7, t8
return None
defined = def_function.function(func)
t = constant_op.constant(0.0)
def cache_computation():
return defined(t1=t, t2=t, t3=t, t4=t, t5=t, t6=t, t7=t, t8=t)
self._run(cache_computation, 30000)
def benchmark_defun_with_signature(self):
def func(t1, t2, t3, t4, t5, t6, t7, t8):
del t1, t2, t3, t4, t5, t6, t7, t8
return None
defined = def_function.function(
func, input_signature=[tensor_spec.TensorSpec([], dtypes.float32)] * 8)
t = constant_op.constant(0.0)
signature_computation = lambda: defined(t, t, t, t, t, t, t, t)
self._run(signature_computation, 30000)
def benchmark_defun_with_signature_and_kwargs(self):
def func(t1, t2, t3, t4, t5, t6, t7, t8):
del t1, t2, t3, t4, t5, t6, t7, t8
return None
defined = def_function.function(
func, input_signature=[tensor_spec.TensorSpec([], dtypes.float32)] * 8)
t = constant_op.constant(0.0)
def signature_computation():
return defined(t1=t, t2=t, t3=t, t4=t, t5=t, t6=t, t7=t, t8=t)
self._run(signature_computation, 30000)
def benchmark_matmul_read_variable_op_2_by_2_CPU(self):
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_matmul_read_variable(m, num_iters=self._num_iters_2_by_2)
def benchmark_matmul_read_variable_op_with_tape_2_by_2_CPU(self):
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_matmul_read_variable_with_tape(
m, num_iters=self._num_iters_2_by_2)
def benchmark_read_variable_op_2_by_2_CPU(self):
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_read_variable(m, num_iters=self._num_iters_2_by_2)
def benchmark_read_variable_op_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2.gpu())
self._benchmark_read_variable(m, num_iters=self._num_iters_2_by_2)
def benchmark_read_variable_op_with_tape_2_by_2_CPU(self):
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2)
self._benchmark_read_variable_with_tape(
m, num_iters=self._num_iters_2_by_2)
def benchmark_read_variable_op_with_tape_2_by_2_GPU(self):
if not context.num_gpus():
return
with context.device(GPU):
m = resource_variable_ops.ResourceVariable(self._m_2_by_2.gpu())
self._benchmark_read_variable_with_tape(
m, num_iters=self._num_iters_2_by_2)
def benchmarkScan(self):
elems = math_ops.range(1600)
def scan():
return functional_ops.scan(
lambda a, x: a + x, elems, parallel_iterations=1)
self._run(scan, 100)
@test_util.disable_tfrt("tf.While not supported RTFB tensor. b/169374895")
def benchmarkScanDefun(self):
elems = math_ops.range(1600)
@def_function.function
def scan():
return functional_ops.scan(
lambda a, x: a + x, elems, parallel_iterations=1)
self._run(scan, 100)
def benchmark_fastpath_conversion_type_inference(self):
c = constant_op.constant(1., dtype=dtypes.float32)
def fn():
return gen_math_ops.add(c, 1)
self._run(fn, 10000)
def benchmark_convert_tensor(self):
value = ops.convert_to_tensor(42)
def fn():
return ops.convert_to_tensor(value)
self._run(fn, 10000)
def _benchmark_convert_constant(self, value, cached):
global GLOBAL_TEST_VALUE
GLOBAL_TEST_VALUE = value
def cached_func():
ops.convert_to_tensor(value)
def uncached_func():
global GLOBAL_TEST_VALUE
GLOBAL_TEST_VALUE += 1
ops.convert_to_tensor(GLOBAL_TEST_VALUE)
func = cached_func if cached else uncached_func
self._run(func, 10000)
def benchmark_convert_python_int(self):
self._benchmark_convert_constant(42, cached=True)
def benchmark_convert_python_int_uncached(self):
self._benchmark_convert_constant(42, cached=False)
def benchmark_convert_python_float(self):
self._benchmark_convert_constant(42.0, cached=True)
def benchmark_convert_python_float_uncached(self):
self._benchmark_convert_constant(42.0, cached=False)
def benchmark_convert_numpy_int(self):
self._benchmark_convert_constant(np.array(42), cached=True)
def benchmark_convert_numpy_int_uncached(self):
self._benchmark_convert_constant(np.array(42), cached=False)
def benchmark_convert_numpy_float(self):
self._benchmark_convert_constant(np.array(42.0), cached=True)
def benchmark_convert_numpy_float_uncached(self):
self._benchmark_convert_constant(np.array(42.0), cached=False)
def benchmark_convert_3x_list_to_tensor(self):
xs = [1, 2, 3]
self._run(lambda: ops.convert_to_tensor(xs), 1000)
def benchmark_convert_3x_array_to_tensor(self):
xs = np.array([1, 2, 3], dtype=np.int32)
self._run(lambda: ops.convert_to_tensor(xs), 1000)
def benchmark_constant_40x2_list_to_tensor(self):
xs = [[0] * 2] * 40
self._run(lambda: constant_op.constant(xs), 1000)
def benchmark_constant_40x2_array_to_tensor(self):
xs = np.array([[0] * 2] * 40, dtype=np.int32)
self._run(lambda: constant_op.constant(xs), 1000)
def benchmark_constant_40x_list_of_2x_arrays_to_tensor(self):
xs = [np.array([0] * 2, dtype=np.int32)] * 40
self._run(lambda: constant_op.constant(xs), 1000)
def benchmark_constant_20x20x20_double_list_to_float32_tensor(self):
xs = [[[np.linspace(0, 1, 21).tolist()] * 20] * 20]
self._run(lambda: constant_op.constant(xs, dtype=dtypes.float32), 10000)
def benchmark_constant_20x20x20_double_list_to_float64_tensor(self):
xs = [[[np.linspace(0, 1, 21).tolist()] * 20] * 20]
self._run(lambda: constant_op.constant(xs, dtype=dtypes.float64), 10000)
def benchmark_list_of_zeros_to_np_array(self):
values = []
for _ in range(1000):
values.append(array_ops.zeros(shape=(1000,)))
self._run(lambda: np.array([x.numpy() for x in values]), 1000)
def benchmark_function_trace(self):
def func(x):
return x
self._run(lambda: (def_function.function(func)(x) for x in range(1000)),
30000)
def _benchmarkFunctionWithResourceInputs(self, num_resources, num_iters):
@def_function.function
def add_all(*args):
return math_ops.add_n(*args)
with context.device(CPU):
resources = []
for _ in range(num_resources):
resources.append(resource_variable_ops.ResourceVariable(self._m_2))
self._run(lambda: add_all(resources), num_iters)
def benchmarkFunctionWithFiveResourceInputs(self):
self._benchmarkFunctionWithResourceInputs(5, 1000)
def benchmarkFunctionWithFiveHundredResourceInputs(self):
self._benchmarkFunctionWithResourceInputs(500, 100)
def _benchmarkResourceReadsInCondInInnerFunc(self, var_count):
rvars = []
for _ in range(var_count):
rvars.append(resource_variable_ops.ResourceVariable(1.0))
# Note: We want to benchmark the graph building time so we intentionally
# add this outer function so that the tf.function gets retraced every time.
def benchmark_fn():
@def_function.function
def fn_with_many_reads():
@def_function.function
def fn_with_many_reads_inner():
def then_branch():
return math_ops.add_n(rvars)
def else_branch():
return 0.
return cond.cond(
constant_op.constant(True), then_branch, else_branch)
return fn_with_many_reads_inner()
return fn_with_many_reads()
with context.device(CPU):
self._run(benchmark_fn, 10)
def benchmarkTenThousandResourceReadsInCondInInnerFunc(self):
self._benchmarkResourceReadsInCondInInnerFunc(10000)
def benchmarkHundredResourceReadsInCondInInnerFunc(self):
self._benchmarkResourceReadsInCondInInnerFunc(100)
def benchmarkTenResourceReadsInCondInInnerFunc(self):
self._benchmarkResourceReadsInCondInInnerFunc(10)
def benchmark_tf_name_scope(self):
def fn():
with ops.name_scope_v2("name"):
pass
self._run(fn, 10000)
def benchmark_tf_nest_map_structure(self):
nested = {"a": [1, 2, 3], "b": (4, 5, 6)}
def fn():
nest.map_structure(lambda x: x, nested)
self._run(fn, 10000)
def benchmark_tf_nest_pack_sequence_as(self):
nested = {"a": [1, 2, 3], "b": (4, 5, 6)}
flat = nest.flatten(nested)
def fn():
nest.pack_sequence_as(nested, flat)
self._run(fn, 10000)
def benchmark_tf_nest_flatten_none(self):
def fn():
nest.flatten(None)
self._run(fn, 100000)
def benchmark_tf_nest_flatten(self):
nested = {"a": [1, 2, 3], "b": (4, 5, 6)}
def fn():
nest.flatten(nested)
self._run(fn, 100000)
def benchmark_tf_flatten_dict_items(self):
nested = {(4, 5, (6, 8)): ("a", "b", ("c", "d"))}
def fn():
nest.flatten_dict_items(nested)
self._run(fn, 100000)
def benchmark_tf_nn_convolution_overhead(self):
inputs = array_ops.ones((1, 1, 1, 1))
filters = array_ops.ones((1, 1, 1, 1))
def fn():
nn_ops.convolution_v2(inputs, filters)
self._run(fn, 10000)
def benchmark_tf_tensor_shape_creation_overhead(self):
# A `TensorShape` is created the first time `EagerTensor.shape` is
# called, which puts `TensorShape.__init__` on the hotpath. The
# `TensorShape` is created from `EagerTensor._shape_tuple`.
x = array_ops.ones((1, 1))
shape_tuple = x._shape_tuple()
def fn():
tensor_shape.TensorShape(shape_tuple)
self._run(fn, 100000)
def _boolean_mask_input(self):
n = 3000
return (array_ops.ones([n, n]), array_ops.fill([n, n], True))
def _boolean_mask_fn(self, input_tensor, mask):
return array_ops.boolean_mask(input_tensor, mask)
def benchmark_tf_boolean_mask_eager(self):
input_tensor, mask = self._boolean_mask_input()
self._run(lambda: self._boolean_mask_fn(input_tensor, mask), 10000)
def benchmark_tf_boolean_mask_graph(self):
input_tensor, mask = self._boolean_mask_input()
compiled_fn = def_function.function(self._boolean_mask_fn)
self._run(lambda: compiled_fn(input_tensor, mask), 10000)
def _benchmark_tf_range_var(self,
limit=100,
dtype=dtypes.int32,
range_dtype=dtypes.int32,
device=CPU,
num_iters=1000):
def func(v, lim):
for _ in math_ops.range(lim, dtype=range_dtype):
v.assign_add(constant_op.constant(1, dtype=dtype))
return v
compiled_func = def_function.function(func)
with context.device(CPU):
m = resource_variable_ops.ResourceVariable(
constant_op.constant(1, dtype=dtype), dtype=dtype)
limit_t = constant_op.constant(limit, dtype=dtype)
with context.device(device):
compiled_func(m, limit_t)
self._run(lambda: compiled_func(m, limit_t), num_iters=num_iters)
def benchmark_tf_range_var_int32_CPU(self):
self._benchmark_tf_range_var()
def benchmark_tf_range_var_int64_CPU(self):
self._benchmark_tf_range_var(dtype=dtypes.int64, range_dtype=dtypes.int64)
def benchmark_tf_range_var_int32_GPU(self):
self._benchmark_tf_range_var(device=GPU)
def benchmark_tf_range_var_int64_GPU(self):
self._benchmark_tf_range_var(
dtype=dtypes.int64, range_dtype=dtypes.int64, device=GPU)
def _benchmark_tf_range_const(self,
limit=100,
dtype=dtypes.int32,
range_dtype=dtypes.int32,
device=CPU,
num_iters=1000):
def func(c, lim):
for _ in math_ops.range(lim, dtype=range_dtype):
c += 1
return c
compiled_func = def_function.function(func)
with context.device(CPU):
input_c = constant_op.constant(1, dtype=dtype)
limit_t = constant_op.constant(limit, dtype=dtype)
with context.device(device):
compiled_func(input_c, limit_t)
self._run(lambda: compiled_func(input_c, limit_t), num_iters=num_iters)
# int32 constant, int32 range, CPU
def benchmark_tf_range_const_int32_int32_CPU(self):
self._benchmark_tf_range_const()
# int32 constant, int64 range, CPU
def benchmark_tf_range_const_int32_int64_CPU(self):
self._benchmark_tf_range_const(range_dtype=dtypes.int64)
# int64 constant, int32 range, CPU
def benchmark_tf_range_const_int64_int32_CPU(self):
self._benchmark_tf_range_const(dtype=dtypes.int64)
# int64 constant, int64 range, CPU
def benchmark_tf_range_const_int64_int64_CPU(self):
self._benchmark_tf_range_const(dtype=dtypes.int64, range_dtype=dtypes.int64)
# int32 constant, int32 range, GPU
def benchmark_tf_range_const_int32_int32_GPU(self):
self._benchmark_tf_range_const(device=GPU)
# int32 constant, int64 range, GPU
def benchmark_tf_range_const_int32_int64_GPU(self):
self._benchmark_tf_range_const(range_dtype=dtypes.int64, device=GPU)
# int64 constant, int32 range, GPU
def benchmark_tf_range_const_int64_int32_GPU(self):
self._benchmark_tf_range_const(dtype=dtypes.int64, device=GPU)
# int64 constant, int64 range, GPU
def benchmark_tf_range_const_int64_int64_GPU(self):
self._benchmark_tf_range_const(
dtype=dtypes.int64, range_dtype=dtypes.int64, device=GPU)
def _benchmark_tf_range_return(self,
limit=100000,
dtype=dtypes.int32,
device=CPU,
num_iters=100000):
def func(lim):
return math_ops.range(lim, dtype=dtype)
compiled_func = def_function.function(func)
with context.device(device):
limit_t = constant_op.constant(limit, dtype=dtype)
compiled_func(limit_t)
self._run(lambda: compiled_func(limit_t), num_iters=num_iters)
def benchmark_tf_range_return_int32_CPU(self):
self._benchmark_tf_range_return()
def benchmark_tf_range_return_int64_CPU(self):
self._benchmark_tf_range_return(dtype=dtypes.int64)
def benchmark_tf_range_return_int32_GPU(self):
self._benchmark_tf_range_return(device=GPU)
def benchmark_tf_range_return_int64_GPU(self):
self._benchmark_tf_range_return(dtype=dtypes.int64, device=GPU)
def _benchmark_embedding_lookup_sparse_with_sparse_input(
self, allow_fast_lookup=True, batch_size=32000, device=GPU
):
def func(sp_ids):
return embedding_ops.embedding_lookup_sparse(
self._m_10000_by_16, sp_ids, None, allow_fast_lookup=allow_fast_lookup
)
with context.device(device):
values = random_ops.random_uniform(
shape=(batch_size,), minval=1, maxval=10000, dtype=dtypes.int64
)
value_rowids = ops.EagerTensor(np.arange(batch_size), device=device)
ragged_input = ragged_tensor.RaggedTensor.from_value_rowids(
values, value_rowids
)
sparse_input = ragged_input.to_sparse()
func(sparse_input)
self._run(lambda: func(sparse_input), num_iters=2000)
def benchmark_tf_embedding_lookup_sparse_with_sparse_input_sparse_grads(self):
self._benchmark_embedding_lookup_sparse_with_sparse_input(
allow_fast_lookup=False
)
def benchmark_tf_embedding_lookup_sparse_with_sparse_input_dense_grads(self):
self._benchmark_embedding_lookup_sparse_with_sparse_input(
allow_fast_lookup=True
)
def _benchmark_embedding_lookup_sparse_with_ragged_input(
self, allow_fast_lookup=True, batch_size=32000, device=GPU
):
def func(sp_ids):
return embedding_ops.embedding_lookup_sparse(
self._m_10000_by_16, sp_ids, None, allow_fast_lookup=allow_fast_lookup
)
with context.device(device):
values = random_ops.random_uniform(
shape=(batch_size,), minval=1, maxval=10000, dtype=dtypes.int64
)
value_rowids = ops.EagerTensor(np.arange(batch_size), device=device)
ragged_input = ragged_tensor.RaggedTensor.from_value_rowids(
values, value_rowids
)
func(ragged_input)
self._run(lambda: func(ragged_input), num_iters=2000)
def benchmark_embedding_lookup_sparse_with_ragged_input_sparse_grads(self):
self._benchmark_embedding_lookup_sparse_with_ragged_input(
allow_fast_lookup=False
)
def benchmark_embedding_lookup_sparse_with_ragged_input_dense_grads(self):
self._benchmark_embedding_lookup_sparse_with_ragged_input(
allow_fast_lookup=True
)
def _RandomIdsAndWeights(self, batch_size, vocab_size, max_val_per_entry):
vals_per_batch_entry = np.random.randint(
1, max_val_per_entry, size=batch_size
)
num_vals = np.sum(vals_per_batch_entry)
ids = np.random.randint(vocab_size, size=num_vals)
weights = 1 + np.random.rand(num_vals)
indices = []
for batch_entry, num_val in enumerate(vals_per_batch_entry):
for val_index in range(num_val):
indices.append([batch_entry, val_index])
shape = [batch_size, max_val_per_entry]
sp_ids = sparse_tensor.SparseTensor(
constant_op.constant(indices, dtypes.int64),
constant_op.constant(ids, dtypes.int32),
constant_op.constant(shape, dtypes.int64),
)
sp_weights = sparse_tensor.SparseTensor(
constant_op.constant(indices, dtypes.int64),
constant_op.constant(weights, dtypes.float32),
constant_op.constant(shape, dtypes.int64),
)
return sp_ids, sp_weights
def _benchmark_embedding_lookup_sparse_with_gradient(
self, params, batch_size, max_val_per_entry, device
):
def func(sp_ids):
with forward_compat.forward_compatibility_horizon(2023, 9, 26):
with gradients.GradientTape() as g:
y = embedding_ops.embedding_lookup_sparse(params, sp_ids, None)
params_grad = g.gradient(y, params)
return params_grad
vocab_size = params.get_shape()[0]
with context.device(device):
sp_ids, _ = self._RandomIdsAndWeights(
batch_size, vocab_size, max_val_per_entry
)
func(sp_ids)
self._run(lambda: func(sp_ids), num_iters=2000)
def benchmark_embedding_lookup_sparse_with_gradient(self):
params = random_ops.random_uniform((1024 * 1024, 16))
params = params.gpu()
params = resource_variable_ops.ResourceVariable(params)
self._benchmark_embedding_lookup_sparse_with_gradient(
params, batch_size=32768, max_val_per_entry=64, device=GPU
)
if __name__ == "__main__":
test.main()