mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
0440f3bf93
Summary: The topk GPU test was taking too much time, but there are still a variety of codepaths to test (k <= 1024, k > 1024, k == 1, k == n). Reduce the batch sizes and n to reduce time taken by the in-python CPU code equivalent. Reviewed By: pietern Differential Revision: D6272628 fbshipit-source-id: b8b8f3601f28bf64f144c73d7c9e915f40c84d70
199 lines
7.5 KiB
Python
199 lines
7.5 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
|
|
#
|
|
# 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.
|
|
##############################################################################
|
|
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
from __future__ import unicode_literals
|
|
|
|
from collections import OrderedDict
|
|
import hypothesis.strategies as st
|
|
import numpy as np
|
|
import random
|
|
|
|
from caffe2.python import core
|
|
from hypothesis import given
|
|
import caffe2.python.hypothesis_test_util as hu
|
|
|
|
|
|
class TestTopK(hu.HypothesisTestCase):
|
|
|
|
def top_k_ref(self, X, k, flatten_indices):
|
|
X_flat = X.reshape((-1, X.shape[-1]))
|
|
indices_ref = np.ndarray(shape=X_flat.shape, dtype=np.int32)
|
|
flatten_indices_ref = np.ndarray(shape=X_flat.shape, dtype=np.int32)
|
|
values_ref = np.ndarray(shape=X_flat.shape, dtype=np.float32)
|
|
global_idx = 0
|
|
for i in range(X_flat.shape[0]):
|
|
od = OrderedDict()
|
|
for j in range(X_flat.shape[1]):
|
|
val = X_flat[i, j]
|
|
if val not in od:
|
|
od[val] = []
|
|
od[val].append((j, global_idx))
|
|
global_idx += 1
|
|
j = 0
|
|
for val, idxs in sorted(od.items(), reverse=True):
|
|
for (idx, flatten_idx) in idxs:
|
|
indices_ref[i, j] = idx
|
|
flatten_indices_ref[i, j] = flatten_idx
|
|
values_ref[i, j] = val
|
|
j = j + 1
|
|
|
|
indices_ref = np.reshape(indices_ref, X.shape)
|
|
flatten_indices_ref = np.reshape(flatten_indices_ref, X.shape)
|
|
values_ref = np.reshape(values_ref, X.shape)
|
|
|
|
indices_ref = indices_ref.take(list(range(k)), axis=-1)
|
|
flatten_indices_ref = flatten_indices_ref.take(list(range(k)), axis=-1)\
|
|
.flatten()
|
|
values_ref = values_ref.take(list(range(k)), axis=-1)
|
|
|
|
if flatten_indices:
|
|
return (values_ref, indices_ref, flatten_indices_ref)
|
|
else:
|
|
return (values_ref, indices_ref)
|
|
|
|
@given(X=hu.tensor(), flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k(self, X, flatten_indices, gc, dc):
|
|
X = X.astype(dtype=np.float32)
|
|
k = random.randint(1, X.shape[-1])
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(bs=st.integers(1, 3), n=st.integers(1, 1), k=st.integers(1, 1),
|
|
flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k_1(self, bs, n, k, flatten_indices, gc, dc):
|
|
X = np.random.rand(bs, n).astype(dtype=np.float32)
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(bs=st.integers(1, 3), n=st.integers(1, 10000), k=st.integers(1, 1),
|
|
flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k_2(self, bs, n, k, flatten_indices, gc, dc):
|
|
X = np.random.rand(bs, n).astype(dtype=np.float32)
|
|
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(bs=st.integers(1, 3), n=st.integers(1, 10000),
|
|
k=st.integers(1, 1024), flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k_3(self, bs, n, k, flatten_indices, gc, dc):
|
|
X = np.random.rand(bs, n).astype(dtype=np.float32)
|
|
k = min(k, n)
|
|
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(bs=st.integers(1, 3), n=st.integers(100, 10000),
|
|
flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k_4(self, bs, n, flatten_indices, gc, dc):
|
|
k = np.random.randint(n // 3, 3 * n // 4)
|
|
X = np.random.rand(bs, n).astype(dtype=np.float32)
|
|
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(bs=st.integers(1, 3), n=st.integers(1, 1024),
|
|
flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k_5(self, bs, n, flatten_indices, gc, dc):
|
|
k = n
|
|
X = np.random.rand(bs, n).astype(dtype=np.float32)
|
|
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(bs=st.integers(1, 3), n=st.integers(1, 5000),
|
|
flatten_indices=st.booleans(), **hu.gcs)
|
|
def test_top_k_6(self, bs, n, flatten_indices, gc, dc):
|
|
k = n
|
|
X = np.random.rand(bs, n).astype(dtype=np.float32)
|
|
|
|
output_list = ["Values", "Indices"]
|
|
if flatten_indices:
|
|
output_list.append("FlattenIndices")
|
|
op = core.CreateOperator("TopK", ["X"], output_list,
|
|
k=k, device_option=gc)
|
|
|
|
def bind_ref(X_loc):
|
|
return self.top_k_ref(X_loc, k, flatten_indices)
|
|
|
|
self.assertReferenceChecks(gc, op, [X], bind_ref)
|
|
|
|
@given(X=hu.tensor(min_dim=2), **hu.gcs)
|
|
def test_top_k_grad(self, X, gc, dc):
|
|
X = X.astype(np.float32)
|
|
k = random.randint(1, X.shape[-1])
|
|
|
|
# this try to make sure adding stepsize (0.05)
|
|
# will not change TopK selections at all
|
|
# since dims max_value = 5 as defined in
|
|
# caffe2/caffe2/python/hypothesis_test_util.py
|
|
for i in range(X.shape[-1]):
|
|
X[..., i] = i * 1.0 / X.shape[-1]
|
|
|
|
op = core.CreateOperator(
|
|
"TopK", ["X"], ["Values", "Indices"], k=k, device_option=gc
|
|
)
|
|
|
|
self.assertGradientChecks(gc, op, [X], 0, [0])
|