[MRG] Tensorflow backend & Benchmarker & Myst_parser (#316)

* First batch of tf methods (to be continued)

* Second batch of method (yet to debug)

* tensorflow for cpu

* add tf requirement

* pep8 + bug

* small changes

* attempt to solve pymanopt bug with tf2

* attempt #2

* attempt #3

* attempt 4

* docstring

* correct pep8 violation introduced in merge conflicts resolution

* attempt 5

* attempt 6

* just a random try

* Revert "just a random try"

This reverts commit 8223e768bfe33635549fb66cca2267514a60ebbf.

* GPU tests for tensorflow

* pep8

* attempt to solve issue with m2r2

* Remove transpose backend method

* first draft of benchmarker (need to correct time measurement)

* prettier bench table

* Bitsize and prettier device methods

* prettified table bench

* Bug corrected (results were mixed up in the final table)

* Better perf counter (for GPU support)

* pep8

* EMD bench

* solve bug if no GPU available

* pep8

* warning about tensorflow numpy api being required in the backend.py docstring

* Bug solve in backend docstring

* not covering code which requires a GPU

* Tensorflow gradients manipulation tested

* Number of warmup runs is now customizable

* typo

* Remove some warnings while building docs

* Change prettier_device to device_type in backend

* Correct JAX mistakes preventing to see the CPU if a GPU is present

* Attempt to solve JAX bug in case no GPU is found

* Reworked benchmarks order and results storage & clear GPU after usage by benchmark

* Add bench to backend docstring

* better benchs

* remove useless stuff

* Better device_type

* Now using MYST_PARSER and solving links issue in the README.md / online docs

1 files changed, 57 insertions, 0 deletions

diff --git a/test/test_sliced.py b/test/test_sliced.py
index 245202c..91e0961 100644
--- a/test/test_sliced.py
+++ b/test/test_sliced.py
@@ -10,6 +10,7 @@ import pytest
 
 import ot
 from ot.sliced import get_random_projections
+from ot.backend import tf
 
 
 def test_get_random_projections():
@@ -161,6 +162,34 @@ def test_sliced_backend_type_devices(nx):
         nx.assert_same_dtype_device(xb, valb)
 
 
+@pytest.mark.skipif(not tf, reason="tf not installed")
+def test_sliced_backend_device_tf():
+    nx = ot.backend.TensorflowBackend()
+    n = 100
+    rng = np.random.RandomState(0)
+    x = rng.randn(n, 2)
+    y = rng.randn(2 * n, 2)
+    P = rng.randn(2, 20)
+    P = P / np.sqrt((P**2).sum(0, keepdims=True))
+
+    # Check that everything stays on the CPU
+    with tf.device("/CPU:0"):
+        xb = nx.from_numpy(x)
+        yb = nx.from_numpy(y)
+        Pb = nx.from_numpy(P)
+        valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
+        nx.assert_same_dtype_device(xb, valb)
+
+    if len(tf.config.list_physical_devices('GPU')) > 0:
+        # Check that everything happens on the GPU
+        xb = nx.from_numpy(x)
+        yb = nx.from_numpy(y)
+        Pb = nx.from_numpy(P)
+        valb = ot.sliced_wasserstein_distance(xb, yb, projections=Pb)
+        nx.assert_same_dtype_device(xb, valb)
+        assert nx.dtype_device(valb)[1].startswith("GPU")
+
+
 def test_max_sliced_backend(nx):
 
     n = 100
@@ -211,3 +240,31 @@ def test_max_sliced_backend_type_devices(nx):
         valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
 
         nx.assert_same_dtype_device(xb, valb)
+
+
+@pytest.mark.skipif(not tf, reason="tf not installed")
+def test_max_sliced_backend_device_tf():
+    nx = ot.backend.TensorflowBackend()
+    n = 100
+    rng = np.random.RandomState(0)
+    x = rng.randn(n, 2)
+    y = rng.randn(2 * n, 2)
+    P = rng.randn(2, 20)
+    P = P / np.sqrt((P**2).sum(0, keepdims=True))
+
+    # Check that everything stays on the CPU
+    with tf.device("/CPU:0"):
+        xb = nx.from_numpy(x)
+        yb = nx.from_numpy(y)
+        Pb = nx.from_numpy(P)
+        valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
+        nx.assert_same_dtype_device(xb, valb)
+
+    if len(tf.config.list_physical_devices('GPU')) > 0:
+        # Check that everything happens on the GPU
+        xb = nx.from_numpy(x)
+        yb = nx.from_numpy(y)
+        Pb = nx.from_numpy(P)
+        valb = ot.max_sliced_wasserstein_distance(xb, yb, projections=Pb)
+        nx.assert_same_dtype_device(xb, valb)
+        assert nx.dtype_device(valb)[1].startswith("GPU")