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https://github.com/saymrwulf/pytorch.git
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27c7158166
Summary: There is a module called `2to3` which you can target for future specifically to remove these, the directory of `caffe2` has the most redundant imports: ```2to3 -f future -w caffe2``` Pull Request resolved: https://github.com/pytorch/pytorch/pull/45033 Reviewed By: seemethere Differential Revision: D23808648 Pulled By: bugra fbshipit-source-id: 38971900f0fe43ab44a9168e57f2307580d36a38
90 lines
3.5 KiB
Python
90 lines
3.5 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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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core, workspace
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from caffe2.quantization.server import dnnlowp_pybind11
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from hypothesis import given, settings
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class TestInt8GenQuantParamsOperator(hu.HypothesisTestCase):
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@settings(max_examples=20, deadline=None)
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@given(
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n=st.integers(10, 100),
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m=st.integers(1, 128),
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k=st.integers(64, 1024),
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quantization_kind=st.sampled_from(
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[
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"MIN_MAX_QUANTIZATION",
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"L2_MIN_QUANTIZATION_APPROX",
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"L2_MIN_QUANTIZATION",
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"P99_QUANTIZATION"
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]
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),
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preserve_sparsity=st.booleans(),
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rnd_seed=st.integers(1, 5),
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**hu.gcs_cpu_only
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)
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def test_int8_gen_quant_params_op(
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self, n, m, k, quantization_kind, preserve_sparsity, rnd_seed, gc, dc
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):
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assert n > 0, "Zero samples in the input data"
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X_min = 0 if preserve_sparsity else -77
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X_max = X_min + 255
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np.random.seed(rnd_seed)
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X = np.round(np.random.rand(n, m, k) * (X_max - X_min) + X_min).astype(
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np.float32
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)
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# Calculate X_qparam
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hist, bin_edges = np.histogram(X.flatten(), bins=2048)
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X_qparam = dnnlowp_pybind11.ChooseStaticQuantizationParams(
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np.min(X), np.max(X), hist, preserve_sparsity, 8, quantization_kind
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)
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# Build a net to generate X's qparam using the Int8GenQuantParams op
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workspace.FeedBlob("X", X, device_option=gc)
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dnnlowp_pybind11.CreateInt8QuantSchemeBlob(
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"quant_scheme", quantization_kind, preserve_sparsity
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)
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assert workspace.HasBlob(
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"quant_scheme"
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), "Failed to create the quant_scheme blob in current workspace"
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gen_quant_params_net = core.Net("gen_quant_params")
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gen_quant_params_op = core.CreateOperator(
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"Int8GenQuantParams",
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["X", "quant_scheme"],
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["quant_param"],
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device_option=gc,
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)
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gen_quant_params_net.Proto().op.extend([gen_quant_params_op])
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assert workspace.RunNetOnce(
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gen_quant_params_net
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), "Failed to run the gen_quant_params net"
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scale, zero_point = dnnlowp_pybind11.ObserveInt8QuantParamsBlob("quant_param")
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shapes, types = workspace.InferShapesAndTypes(
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[gen_quant_params_net],
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blob_dimensions={"X": [n, m, k], "quant_scheme": [1]},
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blob_types={"X": core.DataType.FLOAT, "quant_scheme": core.DataType.STRING}
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)
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self.assertEqual(shapes["quant_param"], [1])
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self.assertEqual(types["quant_param"], core.DataType.FLOAT)
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np.testing.assert_equal(scale, X_qparam.scale)
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np.testing.assert_equal(zero_point, X_qparam.zero_point)
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