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4fcc2fffc3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14958 Test with multiple threads Reviewed By: jianyuh Differential Revision: D13394791 fbshipit-source-id: 931a6c3bda15ebc816807e537dd0841c383e7a6f
70 lines
2.4 KiB
Python
70 lines
2.4 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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import caffe2.python.hypothesis_test_util as hu
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from caffe2.python import core, dyndep, workspace
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from hypothesis import given
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import hypothesis.strategies as st
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import collections
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from dnnlowp_test_utils import check_quantized_results_close
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dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
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workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
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class DNNLowPReluOpTest(hu.HypothesisTestCase):
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@given(size=st.integers(1024, 2048), **hu.gcs_cpu_only)
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def test_dnnlowp_relu(self, size, gc, dc):
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min_ = -10.0
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max_ = 10.0
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scale = (max_ - min_) / 255
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zero_point = int(np.round(-min_ / scale))
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X = (np.random.rand(size) * (max_ - min_) + min_).astype(np.float32)
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Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
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outputs = []
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op_engine_list = [("Relu", ""), ("Relu", "DNNLOWP"), ("Int8Relu", "DNNLOWP")]
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for op_type, engine in op_engine_list:
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net = core.Net("test_net")
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if engine == "DNNLOWP":
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quantize = core.CreateOperator(
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"Quantize",
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["X"],
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["X_q"],
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engine=engine,
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device_option=gc,
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Y_scale=scale,
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Y_zero_point=zero_point,
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)
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net.Proto().op.extend([quantize])
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relu = core.CreateOperator(
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op_type,
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["X_q" if engine == "DNNLOWP" else "X"],
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["Y_q" if engine == "DNNLOWP" else "Y"],
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engine=engine,
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device_option=gc,
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)
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net.Proto().op.extend([relu])
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if engine == "DNNLOWP":
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dequantize = core.CreateOperator(
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"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
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)
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net.Proto().op.extend([dequantize])
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self.ws.create_blob("X").feed(X, device_option=gc)
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self.ws.run(net)
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outputs.append(
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Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
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)
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# Y = max(0, X) so the only error is quantization of inputs
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check_quantized_results_close(outputs, ref=X)
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