mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
964d740ede
Summary:
Added support for batching to SequenceMaskOp.
Let b be the batch dim and k be the axis dim. (We enforce that b < k.) Write the dimensions of the input tensor as [a_1, ..., a_b, ..., a_k, ...]. We first collapse our tensor down to 3D, with dimensions [P, Q, D], where: P = a_1 * ... * a_b, Q=a_{b+1} * ... * a_{k-1}, and D=a_k * a_{k+1} * ... * a_n. Then we mask each slice [i, :, : ] of this 3D tensor (note that each slice is a Q times D tensor w/ dimension 2)
Reviewed By: jamesr66a
Differential Revision: D5733382
fbshipit-source-id: e7a314d9fe6e6691a75112edbee8ba6e8ea8e396
356 lines
14 KiB
Python
356 lines
14 KiB
Python
# Copyright (c) 2016-present, Facebook, Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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##############################################################################
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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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import numpy as np
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from hypothesis import assume, given
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import hypothesis.strategies as st
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core
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import caffe2.python.hypothesis_test_util as hu
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class TestBooleanMaskOp(hu.HypothesisTestCase):
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@given(x=hu.tensor(min_dim=1,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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**hu.gcs)
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def test_boolean_mask(self, x, gc, dc):
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op = core.CreateOperator("BooleanMask",
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["data", "mask"],
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"masked_data")
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mask = np.random.choice(a=[True, False], size=x.shape[0])
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def ref(x, mask):
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return (x[mask],)
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self.assertReferenceChecks(gc, op, [x, mask], ref)
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self.assertDeviceChecks(dc, op, [x, mask], [0])
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@given(x=hu.tensor(min_dim=1,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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**hu.gcs)
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def test_boolean_mask_indices(self, x, gc, dc):
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op = core.CreateOperator("BooleanMask",
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["data", "mask"],
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["masked_data", "masked_indices"])
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mask = np.random.choice(a=[True, False], size=x.shape[0])
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def ref(x, mask):
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return (x[mask], np.where(mask)[0])
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self.assertReferenceChecks(gc, op, [x, mask], ref)
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self.assertDeviceChecks(dc, op, [x, mask], [0])
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@staticmethod
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def _dtype_conversion(x, dtype, gc, dc):
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"""SequenceMask only supports fp16 with CUDA."""
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if dtype == np.float16:
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assume(gc.device_type == caffe2_pb2.CUDA)
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dc = [d for d in dc if d.device_type == caffe2_pb2.CUDA]
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x = x.astype(dtype)
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return x, dc
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@given(x=hu.tensor(min_dim=2,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_sequence_mask_with_lengths(self, x, dtype, gc, dc):
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x, dc = self._dtype_conversion(x, dtype, gc, dc)
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# finite fill value needed for gradient check
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fill_val = 1e-3 if dtype == np.float16 else 1e-9
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op = core.CreateOperator("SequenceMask",
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["data", "lengths"],
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["masked_data"],
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mode="sequence",
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axis=len(x.shape) - 1,
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fill_val=fill_val)
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elem_dim = x.shape[-1]
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leading_dim = 1
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for dim in x.shape[:-1]:
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leading_dim *= dim
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lengths = np.random.randint(0, elem_dim, [leading_dim])\
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.astype(np.int32)
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def ref(x, lengths):
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ref = np.reshape(x, [leading_dim, elem_dim])
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for i in range(leading_dim):
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for j in range(elem_dim):
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if j >= lengths[i]:
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ref[i, j] = fill_val
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return [ref.reshape(x.shape)]
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self.assertReferenceChecks(gc, op, [x, lengths], ref)
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self.assertDeviceChecks(dc, op, [x, lengths], [0])
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@given(x=hu.tensor(min_dim=2,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_sequence_mask_with_window(self, x, dtype, gc, dc):
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x, dc = self._dtype_conversion(x, dtype, gc, dc)
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# finite fill value needed for gradient check
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fill_val = 1e-3 if dtype == np.float16 else 1e-9
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radius = 2
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op = core.CreateOperator("SequenceMask",
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["data", "centers"],
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["masked_data"],
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mode="window",
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radius=radius,
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axis=len(x.shape) - 1,
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fill_val=fill_val)
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elem_dim = x.shape[-1]
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leading_dim = 1
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for dim in x.shape[:-1]:
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leading_dim *= dim
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centers = np.random.randint(0, elem_dim, [leading_dim])\
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.astype(np.int32)
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def ref(x, centers):
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ref = np.reshape(x, [leading_dim, elem_dim])
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for i in range(leading_dim):
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for j in range(elem_dim):
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if j > centers[i] + radius or j < centers[i] - radius:
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ref[i, j] = fill_val
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return [ref.reshape(x.shape)]
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self.assertReferenceChecks(gc, op, [x, centers], ref)
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self.assertDeviceChecks(dc, op, [x, centers], [0])
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threshold = 0.4 if dtype == np.float16 else 0.005
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self.assertGradientChecks(gc, op, [x, centers], 0, [0],
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threshold=threshold)
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@given(x=hu.tensor(min_dim=2,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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mode=st.sampled_from(['upper', 'lower', 'upperdiag', 'lowerdiag']),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_sequence_mask_triangle(self, x, mode, dtype, gc, dc):
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x, dc = self._dtype_conversion(x, dtype, gc, dc)
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# finite fill value needed for gradient check
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fill_val = 1e-3 if dtype == np.float16 else 1e-9
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op = core.CreateOperator("SequenceMask",
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["data"],
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["masked_data"],
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mode=mode,
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axis=len(x.shape) - 1,
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fill_val=fill_val)
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elem_dim = x.shape[-1]
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leading_dim = 1
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for dim in x.shape[:-1]:
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leading_dim *= dim
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if mode == 'upper':
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def compare(i, j):
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return j > i
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elif mode == 'lower':
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def compare(i, j):
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return j < i
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elif mode == 'upperdiag':
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def compare(i, j):
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return j >= i
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elif mode == 'lowerdiag':
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def compare(i, j):
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return j <= i
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def ref(x):
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ref = np.reshape(x, [leading_dim, elem_dim])
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for i in range(leading_dim):
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for j in range(elem_dim):
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if compare(i, j):
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ref[i, j] = fill_val
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return [ref.reshape(x.shape)]
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self.assertReferenceChecks(gc, op, [x], ref)
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self.assertDeviceChecks(dc, op, [x], [0])
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threshold = 0.4 if dtype == np.float16 else 0.005
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stepsize = 0.1 if dtype == np.float16 else 0.05
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self.assertGradientChecks(gc, op, [x], 0, [0],
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threshold=threshold, stepsize=stepsize)
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@given(x=hu.tensor(min_dim=2,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_sequence_mask_batching_lengths(self, x, dtype, gc, dc):
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x, dc = self._dtype_conversion(x, dtype, gc, dc)
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# finite fill value needed for gradient check
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fill_val = 1e-3 if dtype == np.float16 else 1e-9
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# choose _different_ batch and axis dimensions, w/ axis != 0.
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axis = 0
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batch = 0
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while axis == 0 or axis < batch:
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inds = np.arange(len(x.shape))
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np.random.shuffle(inds)
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batch = inds[0]
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axis = inds[1]
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op = core.CreateOperator("SequenceMask",
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["data", "lengths"],
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["masked_data"],
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mode='sequence',
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axis=axis,
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fill_val=fill_val,
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batch=batch)
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before = int(np.prod(x.shape[:batch + 1]))
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between = int(np.prod(x.shape[batch + 1:axis]))
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after = int(np.prod(x.shape[axis:]))
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lengths = np.random.randint(0, after, [between])\
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.astype(np.int32)
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def ref(z, l):
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w = np.reshape(z, [before, between, after])
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for b in range(before):
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r = w[b, :, :]
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for i in range(between):
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for j in range(after):
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if j >= l[i]:
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r[i, j] = fill_val
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return [w.reshape(z.shape)]
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self.assertReferenceChecks(gc, op, [x, lengths], ref)
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self.assertDeviceChecks(dc, op, [x, lengths], [0])
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threshold = 0.4 if dtype == np.float16 else 0.005
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self.assertGradientChecks(gc, op, [x, lengths], 0, [0],
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threshold=threshold)
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@given(x=hu.tensor(min_dim=4,
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max_dim=4,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_sequence_mask_batching_window(self, x, dtype, gc, dc):
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x, dc = self._dtype_conversion(x, dtype, gc, dc)
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# finite fill value needed for gradient check
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fill_val = 1e-3 if dtype == np.float16 else 1e-9
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radius = 1
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# choose _different_ batch and axis dimensions, w/ axis != 0.
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axis = 0
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batch = 0
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while axis == 0 or axis < batch:
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inds = np.arange(len(x.shape))
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np.random.shuffle(inds)
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batch = inds[0]
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axis = inds[1]
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op = core.CreateOperator("SequenceMask",
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["data", "centers"],
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["masked_data"],
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mode='window',
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radius=radius,
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axis=axis,
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fill_val=fill_val,
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batch=batch)
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before = int(np.prod(x.shape[:batch + 1]))
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between = int(np.prod(x.shape[batch + 1:axis]))
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after = int(np.prod(x.shape[axis:]))
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centers = np.random.randint(0, after, [between])\
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.astype(np.int32)
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def ref(z, c):
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w = np.reshape(z, [before, between, after])
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for b in range(before):
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r = w[b, :, :]
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for i in range(between):
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for j in range(after):
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if j > c[i] + radius or j < c[i] - radius:
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r[i, j] = fill_val
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return [w.reshape(z.shape)]
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self.assertReferenceChecks(gc, op, [x, centers], ref)
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self.assertDeviceChecks(dc, op, [x, centers], [0])
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threshold = 0.4 if dtype == np.float16 else 0.005
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self.assertGradientChecks(gc, op, [x, centers], 0, [0],
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threshold=threshold)
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@given(x=hu.tensor(min_dim=3,
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max_dim=5,
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elements=st.floats(min_value=0.5, max_value=1.0)),
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mode=st.sampled_from(['upper', 'lower', 'upperdiag', 'lowerdiag']),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_sequence_mask_batching_triangle(self, x, mode, dtype, gc, dc):
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x, dc = self._dtype_conversion(x, dtype, gc, dc)
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# finite fill value needed for gradient check
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fill_val = 1e-3 if dtype == np.float16 else 1e-9
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# choose _different_ batch and axis dimensions, w/ axis != 0.
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axis = 0
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batch = 0
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while axis == 0 or axis < batch:
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inds = np.arange(len(x.shape))
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np.random.shuffle(inds)
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batch = inds[0]
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axis = inds[1]
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op = core.CreateOperator("SequenceMask",
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["data"],
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["masked_data"],
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mode=mode,
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axis=axis,
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fill_val=fill_val,
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batch=batch)
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if mode == 'upper':
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def compare(i, j):
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return j > i
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elif mode == 'lower':
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def compare(i, j):
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return j < i
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elif mode == 'upperdiag':
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def compare(i, j):
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return j >= i
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elif mode == 'lowerdiag':
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def compare(i, j):
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return j <= i
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def ref(z):
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before = int(np.prod(z.shape[:batch + 1]))
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between = int(np.prod(z.shape[batch + 1:axis]))
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after = int(np.prod(z.shape[axis:]))
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w = np.reshape(z, [before, between, after])
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for b in range(before):
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r = w[b, :, :]
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for i in range(between):
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for j in range(after):
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if compare(i, j):
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r[i, j] = fill_val
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return [w.reshape(z.shape)]
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self.assertReferenceChecks(gc, op, [x], ref)
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self.assertDeviceChecks(dc, op, [x], [0])
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threshold = 0.4 if dtype == np.float16 else 0.005
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stepsize = 0.1 if dtype == np.float16 else 0.05
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self.assertGradientChecks(gc, op, [x], 0, [0],
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threshold=threshold, stepsize=stepsize)
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