rocm_jax/tests/jaxpr_util_test.py

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# Copyright 2020 The JAX Authors.
#
# 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
#
# https://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.
import os
[JAX] Add support for generating "equation profiles" in JAX. An "equation profile" is a pprof profile that maps equations in a jaxpr to the Python stack traces at which they were generated. Pprof can be used a number of ways to analyze and visualize the result. For example, for a profile from a Resnet-50 training step from Flax, we can identify the most common primitives: ``` $ pprof --tags /tmp/myprof Main binary filename not available. primitive: Total 6062.0 1509.0 (24.89%): mul 936.0 (15.44%): add 589.0 ( 9.72%): reshape 492.0 ( 8.12%): div 485.0 ( 8.00%): broadcast_in_dim 330.0 ( 5.44%): reduce_sum 322.0 ( 5.31%): integer_pow 230.0 ( 3.79%): add_any 174.0 ( 2.87%): convert_element_type 160.0 ( 2.64%): select 158.0 ( 2.61%): conv_general_dilated 116.0 ( 1.91%): sub 110.0 ( 1.81%): eq 110.0 ( 1.81%): neg 104.0 ( 1.72%): max 53.0 ( 0.87%): rsqrt 52.0 ( 0.86%): rev 49.0 ( 0.81%): custom_jvp_call_jaxpr 49.0 ( 0.81%): gt 5.0 (0.082%): xla_call 4.0 (0.066%): min 3.0 (0.049%): dot_general 3.0 (0.049%): lt 2.0 (0.033%): cos 2.0 (0.033%): exp 2.0 (0.033%): iota 2.0 (0.033%): log 2.0 (0.033%): psum 2.0 (0.033%): reduce_max 2.0 (0.033%): stop_gradient 1.0 (0.016%): argmax 1.0 (0.016%): reduce_window_max 1.0 (0.016%): select_and_scatter_add 1.0 (0.016%): transpose 1.0 (0.016%): xla_pmap ``` Or the lines of code that generated the most equations: ``` $ pprof --text /tmp/myprof Main binary filename not available. Type: equations Showing nodes accounting for 6038, 99.60% of 6062 total Dropped 5 nodes (cum <= 30) flat flat% sum% cum cum% 1537 25.35% 25.35% 1537 25.35% _compute_stats 1484 24.48% 49.84% 1484 24.48% _normalize 849 14.01% 63.84% 6062 100% __call__ 644 10.62% 74.46% 644 10.62% <unknown> 483 7.97% 82.43% 483 7.97% <unknown> 392 6.47% 88.90% 6061 100% train_step 324 5.34% 94.24% 324 5.34% <unknown> 161 2.66% 96.90% 161 2.66% <unknown> 57 0.94% 97.84% 4292 70.80% loss_fn 52 0.86% 98.70% 52 0.86% schedule 39 0.64% 99.34% 39 0.64% softmax_cross_entropy 8 0.13% 99.47% 30 0.49% compute_metrics 6 0.099% 99.57% 61 1.01% cross_entropy_loss 1 0.016% 99.59% 1321 21.79% apply_gradients 1 0.016% 99.60% 6062 100% train_and_evaluate 0 0% 99.60% 6062 100% <unknown> 0 0% 99.60% 6062 100% __init__ 0 0% 99.60% 3872 63.87% _call_wrapped_method 0 0% 99.60% 6062 100% _run_and_get_tests_result 0 0% 99.60% 6062 100% _run_code_in_main 0 0% 99.60% 6062 100% _run_in_app 0 0% 99.60% 6062 100% _run_main 0 0% 99.60% 3872 63.87% apply 0 0% 99.60% 161 2.66% apply_updates 0 0% 99.60% 6062 100% main 0 0% 99.60% 6062 100% main_function 0 0% 99.60% 6062 100% run 0 0% 99.60% 6062 100% runTests 0 0% 99.60% 6062 100% run_filename_as_main 0 0% 99.60% 6062 100% run_tests 0 0% 99.60% 3872 63.87% scope_fn 0 0% 99.60% 6062 100% test_train_and_evaluate 0 0% 99.60% 1159 19.12% update_fn 0 0% 99.60% 3872 63.87% wrapped_fn 0 0% 99.60% 3872 63.87% wrapped_module_method 0 0% 99.60% 3872 63.87% wrapper ``` I highly recommend the pprof HTTP visualization, using --tagleaf to introduce pseudoframes for each primitive, and to use the "flame" visualization. ``` pprof --tagleaf=primitive --http=:8080 myprof ``` [XLA:Python] Add helpers to Traceback and for working with pprof profiles. * Define hash and equality operators on Tracebacks. * Add functions for converting JSON to and from pprof profile protocol buffers. * Add a helper method that exposes PyCode_Addr2Line to Python. PiperOrigin-RevId: 421395346
2022-01-12 14:27:17 -08:00
import gzip
import json
from absl.testing import absltest
import jax
from jax import jit, make_jaxpr, numpy as jnp
from jax._src import config
from jax._src import jaxpr_util
from jax._src import test_util as jtu
from jax._src.lib import xla_client
config.parse_flags_with_absl()
class JaxprStatsTest(jtu.JaxTestCase):
def test_primitives(self):
def f(x, y):
s = jit(jnp.sin)(x)
return jnp.sin(s) + jnp.cos(y)
hist = jaxpr_util.primitives(make_jaxpr(f)(1., 1.).jaxpr)
primitives = ['add', 'sin', 'cos']
primitives.append('pjit')
for k in primitives:
assert k in hist, k
self.assertEqual(hist['sin'], 2)
self.assertTrue(all(count == 1 for k, count in hist.items() if k != 'sin'))
def test_primitives_by_source(self):
def f(x, y):
s = jnp.sin(x)
return jnp.sin(s) + jnp.cos(y)
hist = jaxpr_util.primitives_by_source(make_jaxpr(f)(1., 1.).jaxpr)
sin_keys = [k for k in hist.keys() if k.startswith('sin @ ')]
rem_keys = [k for k in hist.keys() if not k.startswith('sin @ ')]
self.assertEqual(sum(hist[k] for k in sin_keys), 2)
self.assertTrue(all(hist[k] == 1 for k in rem_keys))
def test_primitives_by_shape(self):
def f(x, y):
def sub(x, y):
return jnp.sum(jnp.array([x, y]))
s = jit(sub)(x, y)
return jnp.sin(s) + jnp.cos(y)
hist = jaxpr_util.primitives_by_shape(make_jaxpr(f)(1., 1.).jaxpr)
t = '64' if config.enable_x64.value else '32'
shapes = [
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f'add :: float{t}[]',
f'sin :: float{t}[]',
f'cos :: float{t}[]',
f'reduce_sum :: float{t}[]',
f'concatenate :: float{t}[2]',
f'pjit :: float{t}[]',
]
for k in shapes:
self.assertEqual(hist[k], 1)
def test_source_locations(self):
def f(x, y):
s = jnp.sin(x) # sin
return jnp.sin(s) + jnp.cos(y) # sin, cos, add
hist = jaxpr_util.source_locations(make_jaxpr(f)(1., 1.).jaxpr)
self.assertEqual(sum(hist.values()), 4)
def test_source_locations_exclude_contextlib(self):
def f(x):
# This generates a stack where the most recent non-jax frame
# comes from contextlib.
return jax.named_call(jnp.cos, name='test')(x)
hist = jaxpr_util.source_locations(make_jaxpr(f)(jnp.arange(8.)).jaxpr)
for filename in hist.keys():
self.assertIn(os.path.basename(__file__), filename)
def test_print_histogram(self):
def f(x, y):
s = jit(jnp.sin)(x)
return jnp.sin(s) + jnp.cos(y)
hist = jaxpr_util.primitives_by_source(make_jaxpr(f)(1., 1.).jaxpr)
jaxpr_util.print_histogram(hist)
[JAX] Add support for generating "equation profiles" in JAX. An "equation profile" is a pprof profile that maps equations in a jaxpr to the Python stack traces at which they were generated. Pprof can be used a number of ways to analyze and visualize the result. For example, for a profile from a Resnet-50 training step from Flax, we can identify the most common primitives: ``` $ pprof --tags /tmp/myprof Main binary filename not available. primitive: Total 6062.0 1509.0 (24.89%): mul 936.0 (15.44%): add 589.0 ( 9.72%): reshape 492.0 ( 8.12%): div 485.0 ( 8.00%): broadcast_in_dim 330.0 ( 5.44%): reduce_sum 322.0 ( 5.31%): integer_pow 230.0 ( 3.79%): add_any 174.0 ( 2.87%): convert_element_type 160.0 ( 2.64%): select 158.0 ( 2.61%): conv_general_dilated 116.0 ( 1.91%): sub 110.0 ( 1.81%): eq 110.0 ( 1.81%): neg 104.0 ( 1.72%): max 53.0 ( 0.87%): rsqrt 52.0 ( 0.86%): rev 49.0 ( 0.81%): custom_jvp_call_jaxpr 49.0 ( 0.81%): gt 5.0 (0.082%): xla_call 4.0 (0.066%): min 3.0 (0.049%): dot_general 3.0 (0.049%): lt 2.0 (0.033%): cos 2.0 (0.033%): exp 2.0 (0.033%): iota 2.0 (0.033%): log 2.0 (0.033%): psum 2.0 (0.033%): reduce_max 2.0 (0.033%): stop_gradient 1.0 (0.016%): argmax 1.0 (0.016%): reduce_window_max 1.0 (0.016%): select_and_scatter_add 1.0 (0.016%): transpose 1.0 (0.016%): xla_pmap ``` Or the lines of code that generated the most equations: ``` $ pprof --text /tmp/myprof Main binary filename not available. Type: equations Showing nodes accounting for 6038, 99.60% of 6062 total Dropped 5 nodes (cum <= 30) flat flat% sum% cum cum% 1537 25.35% 25.35% 1537 25.35% _compute_stats 1484 24.48% 49.84% 1484 24.48% _normalize 849 14.01% 63.84% 6062 100% __call__ 644 10.62% 74.46% 644 10.62% <unknown> 483 7.97% 82.43% 483 7.97% <unknown> 392 6.47% 88.90% 6061 100% train_step 324 5.34% 94.24% 324 5.34% <unknown> 161 2.66% 96.90% 161 2.66% <unknown> 57 0.94% 97.84% 4292 70.80% loss_fn 52 0.86% 98.70% 52 0.86% schedule 39 0.64% 99.34% 39 0.64% softmax_cross_entropy 8 0.13% 99.47% 30 0.49% compute_metrics 6 0.099% 99.57% 61 1.01% cross_entropy_loss 1 0.016% 99.59% 1321 21.79% apply_gradients 1 0.016% 99.60% 6062 100% train_and_evaluate 0 0% 99.60% 6062 100% <unknown> 0 0% 99.60% 6062 100% __init__ 0 0% 99.60% 3872 63.87% _call_wrapped_method 0 0% 99.60% 6062 100% _run_and_get_tests_result 0 0% 99.60% 6062 100% _run_code_in_main 0 0% 99.60% 6062 100% _run_in_app 0 0% 99.60% 6062 100% _run_main 0 0% 99.60% 3872 63.87% apply 0 0% 99.60% 161 2.66% apply_updates 0 0% 99.60% 6062 100% main 0 0% 99.60% 6062 100% main_function 0 0% 99.60% 6062 100% run 0 0% 99.60% 6062 100% runTests 0 0% 99.60% 6062 100% run_filename_as_main 0 0% 99.60% 6062 100% run_tests 0 0% 99.60% 3872 63.87% scope_fn 0 0% 99.60% 6062 100% test_train_and_evaluate 0 0% 99.60% 1159 19.12% update_fn 0 0% 99.60% 3872 63.87% wrapped_fn 0 0% 99.60% 3872 63.87% wrapped_module_method 0 0% 99.60% 3872 63.87% wrapper ``` I highly recommend the pprof HTTP visualization, using --tagleaf to introduce pseudoframes for each primitive, and to use the "flame" visualization. ``` pprof --tagleaf=primitive --http=:8080 myprof ``` [XLA:Python] Add helpers to Traceback and for working with pprof profiles. * Define hash and equality operators on Tracebacks. * Add functions for converting JSON to and from pprof profile protocol buffers. * Add a helper method that exposes PyCode_Addr2Line to Python. PiperOrigin-RevId: 421395346
2022-01-12 14:27:17 -08:00
def test_pprof_equation_profile(self):
def f(x, y):
s = jit(jnp.sin)(x)
return jnp.sin(s) + jnp.cos(y)
profile_gz = jaxpr_util.pprof_equation_profile(make_jaxpr(f)(1., 1.).jaxpr)
profile_proto = gzip.decompress(profile_gz)
json_str = xla_client._xla.pprof_profile_to_json(profile_proto)
profile = json.loads(json_str)
self.assertSetEqual(
{"sampleType", "sample", "stringTable", "location", "function"},
set(profile.keys()))
if __name__ == "__main__":
absltest.main()