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bb9ff58c6d
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/9379 Add cudnn activation ops Reviewed By: houseroad Differential Revision: D8818013 fbshipit-source-id: d3881c634a46578b9331da07f9fdf7e1f31d7e8a
215 lines
6.8 KiB
Python
215 lines
6.8 KiB
Python
from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from __future__ import unicode_literals
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import numpy as np
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from hypothesis import given
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import hypothesis.strategies as st
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.mkl_test_util as mu
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import unittest
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class TestActivations(hu.HypothesisTestCase):
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@given(X=hu.tensor(), in_place=st.booleans(),
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engine=st.sampled_from(["", "CUDNN"]), **mu.gcs)
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def test_relu(self, X, in_place, engine, gc, dc):
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if gc == mu.mkl_do:
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in_place = False
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op = core.CreateOperator(
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"Relu",
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["X"],
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["X"] if in_place else ["Y"],
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engine=engine,
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)
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def relu_ref(X):
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return [np.maximum(X, 0.0)]
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# go away from the origin point to avoid kink problems
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X += 0.02 * np.sign(X)
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X[X == 0.0] += 0.02
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self.assertReferenceChecks(gc, op, [X], relu_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0])
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@unittest.skipIf(not workspace.has_gpu_support,
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"Relu for float16 can only run on GPU now.")
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@given(X=hu.tensor(dtype=np.float16), in_place=st.booleans(),
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engine=st.sampled_from(["", "CUDNN"]), **hu.gcs_gpu_only)
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def test_relu_fp16(self, X, in_place, engine, gc, dc):
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op = core.CreateOperator(
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"Relu",
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["X"],
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["X"] if in_place else ["Y"],
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engine=engine,
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)
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def relu_ref(X):
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return [np.maximum(X, 0.0)]
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def relu_grad_ref(g_out, outputs, fwd_inputs):
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dY = g_out
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[Y] = outputs
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dX = dY
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dX[Y == 0] = 0
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return [dX]
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# go away from the origin point to avoid kink problems
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X += 0.02 * np.sign(X)
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X[X == 0.0] += 0.02
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self.assertReferenceChecks(
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hu.gpu_do,
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op,
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[X],
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relu_ref,
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output_to_grad="X" if in_place else "Y",
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grad_reference=relu_grad_ref)
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@given(X=hu.tensor(elements=st.floats(-3.0, 3.0)),
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n=st.floats(min_value=0.5, max_value=2.0),
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in_place=st.booleans(), **hu.gcs)
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def test_relu_n(self, X, n, in_place, gc, dc):
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op = core.CreateOperator(
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"ReluN",
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["X"],
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["X"] if in_place else ["Y"],
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n=n,
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)
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def relu_n_ref(X):
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return [np.minimum(np.maximum(X, 0.0), n)]
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# go away from 0 and n to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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X -= n
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X += 0.02 * np.sign(X)
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X[X == 0.0] -= 0.02
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X += n
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self.assertReferenceChecks(gc, op, [X], relu_n_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=0.005)
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@given(X=hu.tensor(),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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in_place=st.booleans(), engine=st.sampled_from(["", "CUDNN"]),
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**hu.gcs)
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def test_elu(self, X, alpha, in_place, engine, gc, dc):
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op = core.CreateOperator(
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"Elu",
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["X"],
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["X"] if in_place else ["Y"],
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alpha=alpha,
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engine=engine,
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)
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def elu_ref(X):
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Y = X
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Y[X < 0] = alpha * (np.exp(X[X < 0]) - 1.0)
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return [Y]
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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self.assertReferenceChecks(gc, op, [X], elu_ref)
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self.assertDeviceChecks(dc, op, [X], [0])
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self.assertGradientChecks(gc, op, [X], 0, [0], stepsize=1e-2)
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@given(X=hu.tensor(min_dim=4, max_dim=4),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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inplace=st.booleans(),
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shared=st.booleans(),
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order=st.sampled_from(["NCHW", "NHWC"]),
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seed=st.sampled_from([20, 100]),
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**hu.gcs)
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def test_prelu(self, X, alpha, inplace, shared, order, seed, gc, dc):
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np.random.seed(seed)
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W = np.random.randn(
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X.shape[1] if order == "NCHW" else X.shape[3]).astype(np.float32)
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if shared:
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W = np.random.randn(1).astype(np.float32)
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def prelu_ref(X, W):
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Y = X.copy()
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W = W.reshape(1, -1, 1, 1) if order == "NCHW" \
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else W.reshape(1, 1, 1, -1)
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assert len(X.shape) == 4
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neg_indices = X <= 0
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assert len(neg_indices.shape) == 4
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assert X.shape == neg_indices.shape
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Y[neg_indices] = (Y * W)[neg_indices]
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return (Y,)
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op = core.CreateOperator(
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"PRelu", ["X", "W"], ["Y" if not inplace else "X"],
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alpha=alpha, order=order)
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self.assertReferenceChecks(gc, op, [X, W], prelu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X, W], [0])
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if not inplace:
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# Gradient check wrt X
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self.assertGradientChecks(gc, op, [X, W], 0, [0], stepsize=1e-2)
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# Gradient check wrt W
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self.assertGradientChecks(gc, op, [X, W], 1, [0], stepsize=1e-2)
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@given(X=hu.tensor(),
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alpha=st.floats(min_value=0.1, max_value=2.0),
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inplace=st.booleans(),
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**hu.gcs)
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def test_leaky_relu(self, X, alpha, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def leaky_relu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = Y[neg_indices] * alpha
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return (Y,)
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op = core.CreateOperator(
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"LeakyRelu",
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["X"], ["Y" if not inplace else "X"],
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alpha=alpha)
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self.assertReferenceChecks(gc, op, [X], leaky_relu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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@given(X=hu.tensor(),
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inplace=st.booleans(),
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**hu.gcs)
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def test_leaky_relu_default(self, X, inplace, gc, dc):
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# go away from the origin point to avoid kink problems
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X += 0.04 * np.sign(X)
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X[X == 0.0] += 0.04
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def leaky_relu_ref(X):
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Y = X.copy()
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neg_indices = X <= 0
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Y[neg_indices] = Y[neg_indices] * 0.01
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return (Y,)
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op = core.CreateOperator(
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"LeakyRelu",
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["X"], ["Y" if not inplace else "X"])
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self.assertReferenceChecks(gc, op, [X], leaky_relu_ref)
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# Check over multiple devices
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self.assertDeviceChecks(dc, op, [X], [0])
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