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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
79 lines
2.7 KiB
Python
79 lines
2.7 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import collections
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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, dyndep, workspace
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from dnnlowp_test_utils import check_quantized_results_close
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from hypothesis import given
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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 DNNLowPGatherOpTest(hu.HypothesisTestCase):
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@given(
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dim1=st.integers(256, 512),
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dim2=st.integers(32, 256),
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in_quantized=st.booleans(),
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out_quantized=st.booleans(),
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**hu.gcs_cpu_only
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)
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def test_dnnlowp_gather(self, dim1, dim2, in_quantized, out_quantized, gc, dc):
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# FIXME : DNNLOWP Gather doesn't support quantized input and
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# dequantized output
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if in_quantized:
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out_quantized = True
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data = (np.random.rand(dim1) * 2 - 1).astype(np.float32)
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index = np.floor(np.random.rand(dim2) * dim1).astype(np.int32)
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Output = collections.namedtuple("Output", ["out", "op_type", "engine"])
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outputs = []
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op_engine_list = [
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("Gather", ""),
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("Gather", "DNNLOWP"),
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("Int8Gather", "DNNLOWP"),
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]
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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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do_quantize = "DNNLOWP" in engine and in_quantized
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do_dequantize = "DNNLOWP" in engine and out_quantized
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if do_quantize:
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quantize_data = core.CreateOperator(
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"Quantize", ["data"], ["data_q"], engine=engine, device_option=gc
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)
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net.Proto().op.extend([quantize_data])
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gather = core.CreateOperator(
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op_type,
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["data_q" if do_quantize else "data", "index"],
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["out_q" if do_dequantize else "out"],
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dequantize_output=not do_dequantize,
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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([gather])
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if do_dequantize:
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dequantize = core.CreateOperator(
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"Dequantize", ["out_q"], ["out"], 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("data").feed(data, device_option=gc)
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self.ws.create_blob("index").feed(index, device_option=gc)
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self.ws.run(net)
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outputs.append(
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Output(out=self.ws.blobs["out"].fetch(), op_type=op_type, engine=engine)
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)
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check_quantized_results_close(outputs, ref=data)
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