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"""Test for NCHW[x]c convolution""" import numpy as np import tvm from tvm import te from tvm import autotvm import topi import topi.testing from tvm.contrib.pickle_memoize import memoize from topi.nn.util import get_pad_tuple from topi.util import get_const_tuple from common import get_all_backend def _transform_data(data, bn): # NCHW -> NCHW[x]c batch_size, channel, height, width = data.shape data = np.reshape(data, (batch_size, channel//bn, bn, height, width)) data = np.transpose(data, (0, 1, 3, 4, 2)) return data def _transform_kernel(kernel, ic_bn, oc_bn): # OIHW -> OIHW[x]i[x]o out_channel, in_channel, kh, kw = kernel.shape kernel = np.reshape(kernel, (out_channel//oc_bn, oc_bn, in_channel//ic_bn, ic_bn, kh, kw)) kernel = np.transpose(kernel, (0, 2, 4, 5, 3, 1)) return kernel def _transform_bias(bias, bn): # [num_filter, 1, 1] -> [num_filter//bn, 1, 1, bn] num_filter, h, w = bias.shape bias = np.reshape(bias, (num_filter//bn, bn, h, w)) bias = np.transpose(bias, (0, 2, 3, 1)) return bias def verify_conv2d_NCHWc(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False, dtype="float32"): pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel, kernel)) padding_sum = pad_top + pad_left + pad_bottom + pad_right in_height = in_width = in_size print("Workload: (%d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum)) # for testing functionality, # we choose arbitrary block size that can divide the channel, # regardless of the performance. oc_block = 1 for bn in range(16, 0, -1): if num_filter % bn == 0: oc_block = bn break ic_block = 1 for bn in range(oc_block, 0, -1): if in_channel % bn == 0: ic_block = bn break A = te.placeholder((batch, in_channel//ic_block, in_height, in_width, ic_block), name='A') W = te.placeholder((num_filter//oc_block, in_channel//ic_block, kernel, kernel, ic_block, oc_block), name='W') bias = te.placeholder((num_filter//oc_block, 1, 1, oc_block), name='bias') @memoize("topi.tests.test_topi_conv2d_NCHWc.verify_conv2d_NCHWc") def get_ref_data(): a_np = np.random.uniform(size=(batch, in_channel, in_height, in_width)).astype(dtype) w_np = np.random.uniform(size=(num_filter, in_channel, kernel, kernel)).astype(dtype) b_np = np.random.uniform(size=(num_filter, 1, 1)).astype(dtype) dw_np = topi.testing.dilate_python(w_np, (1, 1, dilation, dilation)) c_np = topi.testing.conv2d_nchw_python(a_np, dw_np, stride, padding) if add_bias: c_np += b_np if add_relu: c_np = np.maximum(c_np, 0) return _transform_data(a_np, ic_block), _transform_kernel(w_np, ic_block, oc_block), \ _transform_bias(b_np, oc_block), _transform_data(c_np, oc_block) a_np, w_np, b_np, c_np = get_ref_data() def check_device(device): ctx = tvm.context(device, 0) if not ctx.exist: print("Skip because %s is not enabled" % device) return print("Running on target: %s" % device) with tvm.target.create(device): C = topi.x86.conv2d_NCHWc(A, W, (stride, stride), padding, (dilation, dilation), 'NCHW%dc'%ic_block, "NCHW%dc"%oc_block, dtype) if add_bias: C = topi.add(C, bias) if add_relu: C = topi.nn.relu(C) s = topi.x86.schedule_conv2d_NCHWc([C]) a = tvm.nd.array(a_np, ctx) w = tvm.nd.array(w_np, ctx) b = tvm.nd.array(b_np, ctx) c = tvm.nd.array(np.zeros(get_const_tuple(C.shape), dtype=C.dtype), ctx) if add_bias: func = tvm.build(s, [A, W, bias, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)) func(a, w, b, c) else: func = tvm.build(s, [A, W, C], device, name="relu_%d_%d_%d_%d_%d_%d_%d_%d" % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)) func(a, w, c) tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=1e-3) # test llvm only for now since conv2d_NCHWc implement is missing in other backend. for device in ["llvm"]: with autotvm.tophub.context(device): # load tophub pre-tuned parameters check_device(device) def test_conv2d_NCHWc(): # ResNet18 workloads verify_conv2d_NCHWc(1, 3, 224, 64, 7, 2, 3) verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1) verify_conv2d_NCHWc(1, 64, 56, 64, 1, 1, 0) verify_conv2d_NCHWc(1, 64, 56, 128, 3, 2, 1) verify_conv2d_NCHWc(1, 64, 56, 128, 1, 2, 0) verify_conv2d_NCHWc(1, 128, 28, 128, 3, 1, 1) verify_conv2d_NCHWc(1, 128, 28, 256, 3, 2, 1) verify_conv2d_NCHWc(1, 128, 28, 256, 1, 2, 0) verify_conv2d_NCHWc(1, 256, 14, 256, 3, 1, 1) verify_conv2d_NCHWc(1, 256, 14, 512, 3, 2, 1) verify_conv2d_NCHWc(1, 256, 14, 512, 1, 2, 0) verify_conv2d_NCHWc(1, 512, 7, 512, 3, 1, 1) # bias, relu verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_relu=True) verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_bias=True) verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, add_bias=True, add_relu=True) # dilation verify_conv2d_NCHWc(1, 64, 56, 64, 3, 1, 1, dilation=2) # batch size verify_conv2d_NCHWc(4, 64, 56, 64, 3, 1, 1) verify_conv2d_NCHWc(9, 64, 56, 64, 3, 1, 1) # weird workloads verify_conv2d_NCHWc(2, 2, 2, 2, 2, 2, 2) verify_conv2d_NCHWc(3, 3, 3, 3, 3, 3, 3) verify_conv2d_NCHWc(4, 4, 4, 4, 4, 4, 4) verify_conv2d_NCHWc(5, 5, 5, 5, 5, 5, 5) verify_conv2d_NCHWc(6, 6, 6, 6, 6, 6, 6) # disable these tests due to some bugs of llvm with nvptx # verify_conv2d_NCHWc(1, 1, 1, 1, 1, 1, 1, dilation=1) # verify_conv2d_NCHWc(1, 1, 1, 1, 1, 1, 1, dilation=2) # verify_conv2d_NCHWc(2, 13, 71, 59, 3, 1, 1) # inception v3 workloads verify_conv2d_NCHWc(1, 3, 299, 32, 3, 2, 0) verify_conv2d_NCHWc(1, 32, 149, 32, 3, 1, 0) verify_conv2d_NCHWc(1, 32, 147, 64, 3, 1, 1) verify_conv2d_NCHWc(1, 64, 73, 80, 1, 1, 0) verify_conv2d_NCHWc(1, 80, 73, 192, 3, 1, 0) verify_conv2d_NCHWc(1, 192, 35, 64, 1, 1, 0) verify_conv2d_NCHWc(1, 192, 35, 48, 1, 1, 0) verify_conv2d_NCHWc(1, 48, 35, 64, 5, 1, 2) verify_conv2d_NCHWc(1, 64, 35, 96, 3, 1, 1) verify_conv2d_NCHWc(1, 96, 35, 96, 3, 1, 1) verify_conv2d_NCHWc(1, 192, 35, 32, 1, 1, 0) verify_conv2d_NCHWc(1, 256, 35, 64, 1, 1, 0) verify_conv2d_NCHWc(1, 256, 35, 48, 1, 1, 0) verify_conv2d_NCHWc(1, 288, 35, 64, 1, 1, 0) verify_conv2d_NCHWc(1, 288, 35, 48, 1, 1, 0) verify_conv2d_NCHWc(1, 288, 35, 384, 3, 2, 0) verify_conv2d_NCHWc(1, 96, 35, 96, 3, 2, 0) verify_conv2d_NCHWc(1, 768, 17, 192, 1, 1, 0) verify_conv2d_NCHWc(1, 768, 17, 128, 1, 1, 0) verify_conv2d_NCHWc(1, 128, 17, 128, 1, 1, 0) verify_conv2d_NCHWc(1, 128, 17, 192, 7, 1, 3) verify_conv2d_NCHWc(1, 128, 17, 128, 7, 1, 3) verify_conv2d_NCHWc(1, 128, 17, 192, 1, 1, 0) verify_conv2d_NCHWc(1, 768, 17, 160, 1, 1, 0) verify_conv2d_NCHWc(1, 160, 17, 160, 1, 1, 0) verify_conv2d_NCHWc(1, 160, 17, 192, 7, 1, 3) verify_conv2d_NCHWc(1, 160, 17, 160, 7, 1, 3) verify_conv2d_NCHWc(1, 160, 17, 192, 1, 1, 0) verify_conv2d_NCHWc(1, 192, 17, 192, 1, 1, 0) verify_conv2d_NCHWc(1, 192, 17, 192, 7, 1, 3) verify_conv2d_NCHWc(1, 192, 17, 320, 3, 2, 0) verify_conv2d_NCHWc(1, 192, 17, 192, 3, 2, 0) verify_conv2d_NCHWc(1, 1280, 8, 320, 1, 1, 0) verify_conv2d_NCHWc(1, 1280, 8, 384, 1, 1, 0) verify_conv2d_NCHWc(1, 384, 8, 384, 1, 1, 0) verify_conv2d_NCHWc(1, 384, 8, 384, 3, 1, 1) verify_conv2d_NCHWc(1, 1280, 8, 448, 1, 1, 0) verify_conv2d_NCHWc(1, 448, 8, 384, 3, 1, 1) verify_conv2d_NCHWc(1, 1280, 8, 192, 1, 1, 0) verify_conv2d_NCHWc(1, 2048, 8, 320, 1, 1, 0) verify_conv2d_NCHWc(1, 2048, 8, 384, 1, 1, 0) verify_conv2d_NCHWc(1, 2048, 8, 448, 1, 1, 0) verify_conv2d_NCHWc(1, 2048, 8, 192, 1, 1, 0) verify_conv2d_NCHWc(1, 1024, 19, 84, 3, 1, 1) verify_conv2d_NCHWc(1, 2048, 10, 126, 3, 1, 1) verify_conv2d_NCHWc(1, 512, 5, 126, 3, 1, 1) verify_conv2d_NCHWc(1, 256, 3, 126, 3, 1, 1) # Asymmetric padding verify_conv2d_NCHWc(1, 32, 17, 64, 7, 2, (0, 0, 1, 1)) verify_conv2d_NCHWc(1, 32, 35, 128, 3, 1, (3, 3, 2, 2)) verify_conv2d_NCHWc(1, 32, 35, 32, 1, 1, (1, 2, 2, 1)) verify_conv2d_NCHWc(1, 32, 17, 192, 1, 1, (1, 2)) verify_conv2d_NCHWc(1, 32, 8, 32, 3, 1, (3, 1)) verify_conv2d_NCHWc(1, 128, 8, 384, 3, 1, (0, 2)) verify_conv2d_NCHWc(1, 32, 8, 32, 1, 1, "VALID") verify_conv2d_NCHWc(1, 388, 8, 32, 3, 1, "VALID") verify_conv2d_NCHWc(1, 512, 19, 32, 1, 1, "SAME") verify_conv2d_NCHWc(1, 32, 10, 32, 2, 1, "SAME") verify_conv2d_NCHWc(1, 32, 8, 32, 3, 1, (1, 2, 2, 1), add_relu=True) verify_conv2d_NCHWc(1, 32, 8, 32, 5, 2, (1, 3), add_bias=True) verify_conv2d_NCHWc(1, 32, 8, 32, 3, 1, "VALID", add_bias=True, add_relu=True) verify_conv2d_NCHWc(1, 32, 8, 32, 24, 1, "SAME", add_bias=True, add_relu=True) if __name__ == "__main__": test_conv2d_NCHWc()