| # 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 -- --benchmarks=. |
| |
| To run GPU benchmarks: |
| bazel run --config=cuda -c opt --copt="-mavx" benchmarks_test -- \ |
| --benchmarks=. |
| |
| 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. --benchmarks=".*matmul*." will run all matmul related benchmarks. |
| |
| """ |
| import time |
| |
| import numpy as np |
| import six |
| |
| from tensorflow.python import pywrap_tfe |
| 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 function |
| 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 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 control_flow_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 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.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 |
| six.raise_from(core._status_to_exception(e), 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)) |
| |
| 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 = function.defun(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 = function.defun(math_ops.matmul) |
| |
| @function.defun |
| 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 = function.defun(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 = function.defun(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 = function.defun( |
| 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 = function.defun( |
| 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) |
| |
| @function.defun |
| 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 control_flow_ops.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) |
| |
| if __name__ == "__main__": |
| test.main() |
