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https://github.com/zebrajr/pytorch.git
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2c6a579859
Summary: It used to be that only the cudnn engine supports it, and now it should be fully supported by any conv engine. To ignore bias, simply use a convolution op that has two inputs instead of 3. The gradient operator will automatically figure out that it does not compute the bias gradient. Reviewed By: prigoyal Differential Revision: D4354183 fbshipit-source-id: cf71b6289a254d15a6a663a85df63fbbaec3702b
336 lines
13 KiB
Python
336 lines
13 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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import numpy as np
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from hypothesis import assume, given, settings
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import hypothesis.strategies as st
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import collections
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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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class TestConvolution(hu.HypothesisTestCase):
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# CUDNN does NOT support different padding values and we skip it
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@given(stride_h=st.integers(1, 3),
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stride_w=st.integers(1, 3),
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pad_t=st.integers(0, 3),
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pad_l=st.integers(0, 3),
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pad_b=st.integers(0, 3),
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pad_r=st.integers(0, 3),
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kernel=st.integers(3, 5),
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size=st.integers(8, 8),
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input_channels=st.integers(1, 3),
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output_channels=st.integers(1, 3),
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batch_size=st.integers(1, 3),
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order=st.sampled_from(["NCHW", "NHWC"]),
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engine=st.sampled_from(["", "EIGEN"]),
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shared_buffer=st.booleans(),
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use_bias=st.booleans(),
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**hu.gcs)
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@settings(max_examples=2, timeout=100)
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def test_convolution_separate_stride_pad_gradients(self, stride_h, stride_w,
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pad_t, pad_l, pad_b,
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pad_r, kernel, size,
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input_channels,
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output_channels,
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batch_size, order,
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engine, shared_buffer,
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use_bias,
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gc, dc):
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op = core.CreateOperator(
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"Conv",
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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stride_h=stride_h,
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stride_w=stride_w,
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pad_t=pad_t,
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pad_l=pad_l,
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pad_b=pad_b,
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pad_r=pad_r,
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kernel=kernel,
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order=order,
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engine=engine,
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shared_buffer=int(shared_buffer),
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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if order == "NCHW":
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X = X.transpose((0, 3, 1, 2))
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w = w.transpose((0, 3, 1, 2))
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inputs = [X, w, b] if use_bias else [X, w]
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self.assertDeviceChecks(dc, op, inputs, [0])
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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# CUDNN does NOT support different padding values and we skip it
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@given(stride_h=st.integers(1, 3),
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stride_w=st.integers(1, 3),
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pad_t=st.integers(0, 3),
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pad_l=st.integers(0, 3),
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pad_b=st.integers(0, 3),
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pad_r=st.integers(0, 3),
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kernel=st.integers(1, 5),
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size=st.integers(7, 10),
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input_channels=st.integers(1, 8),
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output_channels=st.integers(1, 8),
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batch_size=st.integers(1, 3),
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engine=st.sampled_from(["", "EIGEN"]),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_convolution_separate_stride_pad_layout(self, stride_h, stride_w,
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pad_t, pad_l, pad_b, pad_r,
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kernel, size,
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input_channels,
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output_channels, batch_size,
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engine, use_bias, gc, dc):
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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outputs = {}
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for order in ["NCHW", "NHWC"]:
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op = core.CreateOperator(
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"Conv",
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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stride_h=stride_h,
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stride_w=stride_w,
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kernel=kernel,
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pad_t=pad_t,
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pad_l=pad_l,
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pad_b=pad_b,
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pad_r=pad_r,
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order=order,
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engine=engine,
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device_option=gc,
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)
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if order == "NCHW":
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X_f = X.transpose((0, 3, 1, 2))
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w_f = w.transpose((0, 3, 1, 2))
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else:
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X_f = X
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w_f = w
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self.ws.create_blob("X").feed(X_f, device_option=gc)
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self.ws.create_blob("w").feed(w_f, device_option=gc)
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self.ws.create_blob("b").feed(b, device_option=gc)
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self.ws.run(op)
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outputs[order] = self.ws.blobs["Y"].fetch()
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np.testing.assert_allclose(
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outputs["NCHW"],
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outputs["NHWC"].transpose((0, 3, 1, 2)),
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atol=1e-4,
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rtol=1e-4)
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(1, 5),
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dilation=st.integers(1, 3),
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size=st.integers(7, 10),
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input_channels=st.integers(1, 8),
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output_channels=st.integers(1, 8),
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batch_size=st.integers(1, 3),
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order=st.sampled_from(["NCHW", "NHWC"]),
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engine=st.sampled_from(["", "CUDNN", "MKLDNN"]),
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use_bias=st.booleans(),
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**hu.gcs)
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@settings(max_examples=2, timeout=100)
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def test_convolution_gradients(self, stride, pad, kernel, dilation, size,
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input_channels, output_channels, batch_size,
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order, engine, use_bias, gc, dc):
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assume(size >= dilation * (kernel - 1) + 1)
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assume("" == engine or 1 == dilation)
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op = core.CreateOperator(
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"Conv",
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order=order,
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engine=engine,
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)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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if order == "NCHW":
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X = X.transpose((0, 3, 1, 2))
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w = w.transpose((0, 3, 1, 2))
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inputs = [X, w, b] if use_bias else [X, w]
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self.assertDeviceChecks(dc, op, inputs, [0])
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for i in range(len(inputs)):
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self.assertGradientChecks(gc, op, inputs, i, [0])
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@given(stride=st.integers(1, 3),
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pad=st.integers(0, 3),
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kernel=st.integers(1, 5),
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dilation=st.integers(1, 3),
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size=st.integers(7, 10),
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input_channels=st.integers(1, 8),
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output_channels=st.integers(1, 8),
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batch_size=st.integers(1, 3),
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use_bias=st.booleans(),
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**hu.gcs)
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def test_convolution_layout(self, stride, pad, kernel, dilation, size,
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input_channels, output_channels, batch_size,
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use_bias, gc, dc):
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assume(size >= dilation * (kernel - 1) + 1)
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X = np.random.rand(
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batch_size, size, size, input_channels).astype(np.float32) - 0.5
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w = np.random.rand(
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output_channels, kernel, kernel, input_channels).astype(np.float32)\
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- 0.5
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b = np.random.rand(output_channels).astype(np.float32) - 0.5
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Output = collections.namedtuple("Output", ["Y", "engine", "order"])
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outputs = []
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for order in ["NCHW", "NHWC"]:
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for engine in (["", "CUDNN"] if dilation == 1 else [""]):
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op = core.CreateOperator(
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"Conv",
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["X", "w", "b"] if use_bias else ["X", "w"],
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["Y"],
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stride=stride,
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kernel=kernel,
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dilation=dilation,
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pad=pad,
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order=order,
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engine=engine,
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device_option=gc,
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)
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if order == "NCHW":
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X_f = X.transpose((0, 3, 1, 2))
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w_f = w.transpose((0, 3, 1, 2))
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else:
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X_f = X
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w_f = w
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self.assertDeviceChecks(
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dc,
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op,
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[X_f, w_f, b] if use_bias else [X_f, w_f],
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[0])
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self.ws.create_blob("X").feed(X_f, device_option=gc)
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self.ws.create_blob("w").feed(w_f, device_option=gc)
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self.ws.create_blob("b").feed(b, device_option=gc)
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self.ws.run(op)
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outputs.append(Output(
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Y=self.ws.blobs["Y"].fetch(), engine=engine, order=order))
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def canonical(o):
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if o.order == "NHWC":
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return o.Y.transpose((0, 3, 1, 2))
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else:
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return o.Y
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for o in outputs:
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np.testing.assert_allclose(
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canonical(outputs[0]),
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canonical(o),
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atol=1e-4,
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rtol=1e-4)
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@given(num_workers=st.integers(1, 4),
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net_type=st.sampled_from(
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["simple", "dag"] +
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(["async_dag"] if workspace.has_gpu_support else [])),
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do=st.sampled_from(hu.device_options),
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engine=st.sampled_from(["CUDNN", ""]))
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def test_convolution_sync(self, net_type, num_workers, do, engine):
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from caffe2.python.cnn import CNNModelHelper
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m = CNNModelHelper()
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n = 1
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d = 2
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depth = 3
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iters = 5
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h = 5
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w = 5
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workspace.ResetWorkspace()
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np.random.seed(1701)
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# Build a binary tree of conv layers, summing at each node.
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for i in reversed(range(depth)):
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for j in range(2 ** i):
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bottom_1 = "{}_{}".format(i + 1, 2 * j)
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bottom_2 = "{}_{}".format(i + 1, 2 * j + 1)
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mid_1 = "{}_{}_m".format(i + 1, 2 * j)
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mid_2 = "{}_{}_m".format(i + 1, 2 * j + 1)
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top = "{}_{}".format(i, j)
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w1, b1, w2, b2 = np.random.randn(4).tolist()
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m.Conv(
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bottom_1, mid_1,
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dim_in=d, dim_out=d,
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kernel=3,
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weight_init=m.ConstantInit(w1),
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bias_init=m.ConstantInit(b1),
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cudnn_state=np.random.randint(0, 3),
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stride=1,
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pad=1,
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deterministic=1,
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engine=engine)
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m.Conv(
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bottom_2, mid_2,
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dim_in=d, dim_out=d,
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kernel=3,
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stride=1,
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pad=1,
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weight_init=m.ConstantInit(w2),
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bias_init=m.ConstantInit(b2),
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deterministic=1,
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cudnn_state=np.random.randint(0, 3),
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engine=engine)
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m.net.Sum([mid_1, mid_2], top)
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m.net.Flatten(["0_0"], ["0_0_flat"])
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m.net.SquaredL2Distance(["0_0_flat", "label"], "xent")
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m.net.AveragedLoss("xent", "loss")
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input_to_grad = m.AddGradientOperators(["loss"])
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m.Proto().device_option.CopyFrom(do)
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m.param_init_net.Proto().device_option.CopyFrom(do)
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m.Proto().type = net_type
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m.Proto().num_workers = num_workers
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self.ws.run(m.param_init_net)
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def run():
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import numpy as np
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np.random.seed(1701)
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input_blobs = ["{}_{}".format(depth, j) for j in range(2 ** depth)]
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for input_blob in input_blobs:
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self.ws.create_blob(input_blob).feed(
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np.random.randn(n, d, h, w).astype(np.float32),
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device_option=do)
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self.ws.create_blob("label").feed(
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np.random.randn(n, d * h * w).astype(np.float32),
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device_option=do)
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self.ws.run(m.net)
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gradients = [
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self.ws.blobs[str(input_to_grad[input_blob])].fetch()
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for input_blob in input_blobs]
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return gradients
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outputs = [run() for _ in range(iters)]
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for output in outputs[1:]:
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np.testing.assert_array_equal(outputs[0], output)
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np.testing.assert_allclose(
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np.sum(np.square(output)),
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1763719461732352.0,
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rtol=1e-5)
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if __name__ == "__main__":
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import unittest
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unittest.main()
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