mirror of
https://github.com/zebrajr/pytorch.git
synced 2025-12-07 00:21:07 +01:00
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
382 lines
12 KiB
Python
382 lines
12 KiB
Python
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import unittest
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from functools import reduce
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import hypothesis.strategies as st
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from hypothesis import given, settings
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import numpy as np
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from caffe2.proto import caffe2_pb2
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from caffe2.python import core, workspace
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import caffe2.python.hypothesis_test_util as hu
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import caffe2.python.ideep_test_util as mu
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@unittest.skipIf(not workspace.C.use_mkldnn, "No MKLDNN support.")
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class FcTest(hu.HypothesisTestCase):
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@given(n=st.integers(1, 5), m=st.integers(1, 5),
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k=st.integers(1, 5), **mu.gcs)
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@settings(deadline=1000)
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def test_fc_2_dims(self, n, m, k, gc, dc):
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X = np.random.rand(m, k).astype(np.float32) - 0.5
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W = np.random.rand(n, k).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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op = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"]
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)
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self.assertDeviceChecks(dc, op, [X, W, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, W, b], i, [0])
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@given(n=st.integers(1, 5),
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m=st.integers(1, 5),
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c=st.integers(1, 5),
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h=st.integers(1, 5),
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w=st.integers(1, 5),
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axis=st.integers(1, 3),
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**mu.gcs)
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def test_fc_with_axis(self, n, m, c, h, w, axis, gc, dc):
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X = np.random.rand(n, c, h, w).astype(np.float32) - 0.5
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k = reduce((lambda x, y: x * y), [n, c, h, w][axis - 4:])
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nn = reduce((lambda x, y: x * y), [n, c, h, w][:axis])
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W = np.random.rand(m, k).astype(np.float32) - 0.5
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b = np.random.rand(m).astype(np.float32) - 0.5
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dY = np.random.rand(nn, m).astype(np.float32) - 0.5
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op0 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis=axis,
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device_option=dc[0]
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)
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op0_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis=axis,
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device_option=dc[0]
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)
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workspace.ResetWorkspace()
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workspace.FeedBlob('X', X, dc[0])
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workspace.FeedBlob('W', W, dc[0])
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workspace.FeedBlob('b', b, dc[0])
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workspace.RunOperatorOnce(op0)
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Y0 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[0])
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workspace.RunOperatorOnce(op0_bw)
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dW0 = workspace.FetchBlob('dW')
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db0 = workspace.FetchBlob('db')
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op1 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis=axis,
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device_option=dc[1]
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)
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op1_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis=axis,
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device_option=dc[1]
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)
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X', X, dc[1])
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workspace.FeedBlob('W', W, dc[1])
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workspace.FeedBlob('b', b, dc[1])
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workspace.RunOperatorOnce(op1)
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Y1 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[1])
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workspace.RunOperatorOnce(op1_bw)
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dW1 = workspace.FetchBlob('dW')
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db1 = workspace.FetchBlob('db')
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Y0 = Y0.flatten()
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Y1 = Y1.flatten()
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1)
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print(Y0)
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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dW0 = dW0.flatten()
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dW1 = dW1.flatten()
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if not np.allclose(dW0, dW1, atol=0.01, rtol=0.01):
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print(dW1)
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print(dW0)
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print(np.max(np.abs(dW1 - dW0)))
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self.assertTrue(False)
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db0 = db0.flatten()
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db1 = db1.flatten()
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if not np.allclose(db0, db1, atol=0.01, rtol=0.01):
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print(db1)
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print(db0)
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print(np.max(np.abs(db1 - db0)))
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self.assertTrue(False)
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@given(n=st.integers(1, 5),
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o=st.integers(1, 5),
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i=st.integers(1, 5),
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h=st.integers(1, 5),
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w=st.integers(1, 5),
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axis_w=st.integers(1, 3),
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**mu.gcs)
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@settings(deadline=1000)
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def test_fc_with_axis_w(self, n, o, i, h, w, axis_w, gc, dc):
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W = np.random.rand(o, i, h, w).astype(np.float32) - 0.5
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k = reduce((lambda x, y: x * y), [o, i, h, w][axis_w - 4:])
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m = reduce((lambda x, y: x * y), [o, i, h, w][:axis_w])
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X = np.random.rand(n, k).astype(np.float32) - 0.5
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b = np.random.rand(m).astype(np.float32) - 0.5
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dY = np.random.rand(n, m).astype(np.float32) - 0.5
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op0 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis_w=axis_w,
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device_option=dc[0]
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)
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op0_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis_w=axis_w,
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device_option=dc[0]
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)
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workspace.ResetWorkspace()
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workspace.FeedBlob('X', X, dc[0])
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workspace.FeedBlob('W', W, dc[0])
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workspace.FeedBlob('b', b, dc[0])
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workspace.RunOperatorOnce(op0)
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Y0 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[0])
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workspace.RunOperatorOnce(op0_bw)
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dW0 = workspace.FetchBlob('dW')
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db0 = workspace.FetchBlob('db')
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op1 = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"],
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axis_w=axis_w,
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device_option=dc[1]
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)
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op1_bw = core.CreateOperator(
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'FCGradient',
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['X', 'W', 'dY'],
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["dW", "db"],
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axis_w=axis_w,
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device_option=dc[1]
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)
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X', X, dc[1])
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workspace.FeedBlob('W', W, dc[1])
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workspace.FeedBlob('b', b, dc[1])
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workspace.RunOperatorOnce(op1)
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Y1 = workspace.FetchBlob('Y')
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workspace.FeedBlob('dY', dY, dc[1])
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workspace.RunOperatorOnce(op1_bw)
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dW1 = workspace.FetchBlob('dW')
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db1 = workspace.FetchBlob('db')
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Y0 = Y0.flatten()
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Y1 = Y1.flatten()
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if not np.allclose(Y0, Y1, atol=0.01, rtol=0.01):
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print(Y1)
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print(Y0)
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print(np.max(np.abs(Y1 - Y0)))
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self.assertTrue(False)
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dW0 = dW0.flatten()
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dW1 = dW1.flatten()
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if not np.allclose(dW0, dW1, atol=0.01, rtol=0.01):
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print(dW1)
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print(dW0)
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print(np.max(np.abs(dW1 - dW0)))
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self.assertTrue(False)
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db0 = db0.flatten()
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db1 = db1.flatten()
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if not np.allclose(db0, db1, atol=0.01, rtol=0.01):
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print(db1)
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print(db0)
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print(np.max(np.abs(db1 - db0)))
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self.assertTrue(False)
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@given(n=st.integers(1, 5), m=st.integers(1, 5),
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k=st.integers(1, 5), **mu.gcs)
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@settings(deadline=10000)
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def test_fc_4_dims_src(self, n, m, k, gc, dc):
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X = np.random.rand(m, k, m, m).astype(np.float32) - 0.5
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W = np.random.rand(n, k * m * m).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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op = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"]
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)
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self.assertDeviceChecks(dc, op, [X, W, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, W, b], i, [0])
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@given(n=st.integers(1, 5), m=st.integers(1, 5),
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k=st.integers(1, 5), **mu.gcs)
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@settings(deadline=10000)
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def test_fc_4_dims(self, n, m, k, gc, dc):
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X = np.random.rand(m, k, m, m).astype(np.float32) - 0.5
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W = np.random.rand(n, k, m, m).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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op = core.CreateOperator(
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'FC',
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['X', 'W', 'b'],
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["Y"]
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)
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self.assertDeviceChecks(dc, op, [X, W, b], [0])
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for i in range(3):
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self.assertGradientChecks(gc, op, [X, W, b], i, [0])
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@given(n=st.integers(2, 5), m=st.integers(2, 5),
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k=st.integers(2, 5), **mu.gcs_cpu_ideep)
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def test_int8_fc_4_dims(self, n, m, k, gc, dc):
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X = np.random.rand(m, k, m, m).astype(np.float32) - 0.5
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w = np.random.rand(n, k, m, m).astype(np.float32) - 0.5
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b = np.random.rand(n).astype(np.float32) - 0.5
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fc_fp32 = core.CreateOperator(
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'FC',
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['X', 'w', 'b'],
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["Y"]
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)
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old_ws_name = workspace.CurrentWorkspace()
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workspace.SwitchWorkspace("_device_check_", True)
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workspace.FeedBlob('X', X, dc[0])
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workspace.FeedBlob('w', w, dc[0])
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workspace.FeedBlob('b', b, dc[0])
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workspace.RunOperatorOnce(fc_fp32)
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Y = workspace.FetchBlob('Y')
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workspace.ResetWorkspace()
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Y_absmax = np.array([np.absolute(Y).max()]).astype(np.float32)
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if Y.min() >= 0:
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Y_scale = Y_absmax / 0xFF
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Y_zero_point = 0
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else:
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Y_scale = Y_absmax / 0x7F
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Y_zero_point = 128
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X_absmax = np.array([np.absolute(X).max()]).astype(np.float32)
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if X.min() >= 0:
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X_scale = X_absmax / 0xFF
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X_zero_point = 0
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else:
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X_scale = X_absmax / 0x7F
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X_zero_point = 128
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w_absmax = np.array([np.absolute(w[i, ...]).max() for i in range(w.shape[0])]).astype(np.float32)
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w_scale = w_absmax / 0x7F
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w_zero_point = 128
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w = np.transpose(w, (0, 2, 3, 1)).astype(np.float32)
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w_bytes = np.rint([w[i, ...] / w_scale[i] for i in range(w.shape[0])]).astype(np.int8) + w_zero_point
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w_filler = core.CreateOperator(
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"Int8GivenTensorFill",
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[], ["wi"],
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shape=w.shape,
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values=w_bytes.astype(np.uint8).tobytes(),
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Y_zero_point=w_zero_point,
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Y_scales=w_scale,
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device_option=dc[1],
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)
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b_scale = w_scale * X_scale
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b_zero_point = 0
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b_bytes = np.rint([b[i] / b_scale[i] for i in range(b.shape[0])]).astype(np.int32)
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b_filler = core.CreateOperator(
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"Int8GivenIntTensorFill",
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[], ["bi"],
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shape=b.shape,
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values=b_bytes,
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Y_zero_point=b_zero_point,
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Y_scales=b_scale,
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device_option=dc[1],
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)
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sw2nhwc = core.CreateOperator(
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"NCHW2NHWC",
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["Xi"],
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["Xi_nhwc"],
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device_option=dc[1]
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)
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quantize_X = core.CreateOperator(
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"Int8Quantize",
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["Xi_nhwc"],
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["Xi_quantized"],
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engine="DNNLOWP",
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device_option=dc[1],
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Y_zero_point=X_zero_point,
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Y_scale=X_scale[0],
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)
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fc = core.CreateOperator(
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'Int8FC',
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['Xi_quantized', 'wi', 'bi'],
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["Y_out"],
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engine="DNNLOWP",
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device_option=dc[1],
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Y_zero_point=Y_zero_point,
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Y_scale=Y_scale[0],
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)
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net = caffe2_pb2.NetDef()
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net.op.extend([w_filler, b_filler, sw2nhwc, quantize_X, fc])
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workspace.FeedBlob("Xi", X, dc[1])
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workspace.RunNetOnce(net)
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Y_out = workspace.FetchBlob("Y_out")
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MSE = np.square(np.subtract(Y, Y_out)).mean()
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if MSE > 0.005:
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print(Y.flatten())
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print(Y_out.flatten())
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print(np.max(np.abs(Y_out - Y)))
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print("MSE", MSE)
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self.assertTrue(False)
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workspace.SwitchWorkspace(old_ws_name)
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if __name__ == "__main__":
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unittest.main()
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