mirror of
https://github.com/saymrwulf/pytorch.git
synced 2026-05-15 21:00:47 +00:00
d2453afb1e
Added a caffe2 math sum operator so that it takes integers (only int32) Changed the SumFloatIter to SumGenericIter so that it takes >1 types. Added a sumElementInt operator
167 lines
4.1 KiB
Python
167 lines
4.1 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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from caffe2.python import core
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from hypothesis import assume, given
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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.proto import caffe2_pb2
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class TestReductionOps(hu.HypothesisTestCase):
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@given(n=st.integers(5, 8), **hu.gcs)
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def test_elementwise_sum(self, n, gc, dc):
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X = np.random.rand(n).astype(np.float32)
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def sum_op(X):
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return [np.sum(X)]
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op = core.CreateOperator(
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"SumElements",
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["X"],
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["y"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=sum_op,
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)
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self.assertGradientChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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outputs_to_check=0,
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outputs_with_grads=[0],
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)
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@given(n=st.integers(5, 8), **hu.gcs)
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def test_elementwise_int_sum(self, n, gc, dc):
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X = np.random.rand(n).astype(np.int32)
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def sum_op(X):
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return [np.sum(X)]
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op = core.CreateOperator(
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"SumElementsInt",
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["X"],
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["y"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=sum_op,
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)
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@given(n=st.integers(1, 65536),
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dtype=st.sampled_from([np.float32, np.float16]),
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**hu.gcs)
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def test_elementwise_sqrsum(self, n, dtype, gc, dc):
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if dtype == np.float16:
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# fp16 is only supported with CUDA
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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 = np.random.rand(n).astype(dtype)
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def sumsqr_op(X):
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return [np.sum(X * X)]
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op = core.CreateOperator(
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"SumSqrElements",
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["X"],
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["y"]
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)
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threshold = 0.01 if dtype == np.float16 else 0.005
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=sumsqr_op,
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threshold=threshold,
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)
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@given(n=st.integers(5, 8), **hu.gcs)
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def test_elementwise_avg(self, n, gc, dc):
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X = np.random.rand(n).astype(np.float32)
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def avg_op(X):
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return [np.mean(X)]
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op = core.CreateOperator(
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"SumElements",
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["X"],
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["y"],
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average=1
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=avg_op,
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)
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self.assertGradientChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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outputs_to_check=0,
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outputs_with_grads=[0],
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)
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@given(batch_size=st.integers(1, 3),
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m=st.integers(1, 3),
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n=st.integers(1, 4),
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**hu.gcs)
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def test_rowwise_max(self, batch_size, m, n, gc, dc):
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X = np.random.rand(batch_size, m, n).astype(np.float32)
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def rowwise_max(X):
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return [np.max(X, axis=2)]
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op = core.CreateOperator(
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"RowwiseMax",
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["x"],
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["y"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=rowwise_max,
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)
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@given(batch_size=st.integers(1, 3),
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m=st.integers(1, 3),
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n=st.integers(1, 4),
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**hu.gcs)
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def test_columnwise_max(self, batch_size, m, n, gc, dc):
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X = np.random.rand(batch_size, m, n).astype(np.float32)
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def columnwise_max(X):
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return [np.max(X, axis=1)]
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op = core.CreateOperator(
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"ColwiseMax",
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["x"],
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["y"]
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)
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self.assertReferenceChecks(
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device_option=gc,
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op=op,
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inputs=[X],
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reference=columnwise_max,
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)
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