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"""Example code to do convolution.""" import numpy as np import tvm from tvm import te from tvm import autotvm from tvm.autotvm.task.space import FallbackConfigEntity 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, Int8Fallback oc_block_factor = 4 def verify_conv2d_NCHWc_int8(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False): pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel, kernel)) padding_sum = pad_top + pad_left + pad_bottom + pad_right print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)) in_height = in_width = in_size A = te.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='int8') W = te.placeholder((num_filter, in_channel, kernel, kernel), name='W', dtype='int8') bias = te.placeholder((num_filter // oc_block_factor, 1, 1, oc_block_factor), name='bias', dtype='int8') a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) bias_shape = get_const_tuple(bias.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv2d_int8.verify_conv2d_nchw") def get_ref_data(): a_np = np.random.randint(low=-128, high=127, size=a_shape).astype(dtype) w_np = np.random.randint(low=-128, high=128, size=w_shape).astype(dtype) b_np = np.random.uniform(size=bias_shape).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).astype(dtype) # convert to NCHWc _, _, out_height, out_width = c_np.shape c_np = c_np.reshape((batch, num_filter // oc_block_factor, oc_block_factor, \ out_height, out_width)).transpose(0, 1, 3, 4, 2) if add_bias: b_np = np.random.uniform(size=bias_shape).astype(dtype) c_np += b_np if add_relu: c_np = np.maximum(c_np, 0) return a_np, w_np, b_np, c_np 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 if device == "cuda" and not tvm.contrib.nvcc.have_int8(ctx.compute_version): print("Skip because int8 intrinsics are not available") return print("Running on target: %s" % device) with tvm.target.create(device): C = topi.cuda.conv2d_NCHWc_int8(A, W, (stride, stride), padding, (dilation, dilation), 'NCHW', dtype) if add_bias: C = topi.add(C, bias) if add_relu: C = topi.nn.relu(C) s = topi.cuda.schedule_conv2d_NCHWc_int8([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: 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 = 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-5) for device in ["cuda"]: check_device(device) def verify_conv2d_nchw_int8(batch, in_channel, in_size, num_filter, kernel, stride, padding, dilation=1, add_bias=False, add_relu=False): pad_top, pad_left, pad_bottom, pad_right = get_pad_tuple(padding, (kernel, kernel)) padding_sum = pad_top + pad_left + pad_bottom + pad_right print("Workload: (%d, %d, %d, %d, %d, %d, %d, %d)" % (batch, in_channel, in_size, num_filter, kernel, stride, padding_sum, dilation)) in_height = in_width = in_size A = te.placeholder((batch, in_channel, in_height, in_width), name='A', dtype='int8') W = te.placeholder((num_filter, in_channel, kernel, kernel), name='W', dtype='int8') bias = te.placeholder((num_filter, 1, 1), name='bias', dtype='int8') a_shape = get_const_tuple(A.shape) w_shape = get_const_tuple(W.shape) bias_shape = get_const_tuple(bias.shape) dtype = A.dtype @memoize("topi.tests.test_topi_conv2d_int8.verify_conv2d_nchw") def get_ref_data(): a_np = np.random.randint(low=-128, high=127, size=a_shape).astype(dtype) w_np = np.random.randint(low=-128, high=128, size=w_shape).astype(dtype) b_np = np.random.uniform(size=bias_shape).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).astype(dtype) if add_bias: b_np = np.random.uniform(size=bias_shape).astype(dtype) c_np += b_np if add_relu: c_np = np.maximum(c_np, 0) return a_np, w_np, b_np, c_np 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 if device == "cuda" and not tvm.contrib.nvcc.have_int8(ctx.compute_version): print("Skip because int8 intrinsics are not available") return print("Running on target: %s" % device) with tvm.target.create(device): C = topi.cuda.conv2d_nchw_int8(A, W, (stride, stride), padding, (dilation, dilation), dtype) if add_bias: C = topi.add(C, bias) if add_relu: C = topi.nn.relu(C) s = topi.cuda.schedule_conv2d_nchw_int8([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: 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 = 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-5) for device in ["cuda"]: check_device(device) def test_conv2d_nchw(): with Int8Fallback(): # ResNet18 workloads where channels in / out are multiple of oc_block_factor verify_conv2d_NCHWc_int8(1, 64, 56, 64, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 64, 56, 64, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 64, 56, 128, 3, 2, 1) verify_conv2d_NCHWc_int8(1, 64, 56, 128, 1, 2, 0) verify_conv2d_NCHWc_int8(1, 128, 28, 128, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 128, 28, 256, 3, 2, 1) verify_conv2d_NCHWc_int8(1, 128, 28, 256, 1, 2, 0) verify_conv2d_NCHWc_int8(1, 256, 14, 256, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 256, 14, 512, 3, 2, 1) verify_conv2d_NCHWc_int8(1, 256, 14, 512, 1, 2, 0) verify_conv2d_NCHWc_int8(1, 512, 7, 512, 3, 1, 1) # bias, relu verify_conv2d_NCHWc_int8(1, 64, 56, 64, 3, 1, 1, add_relu=True) verify_conv2d_NCHWc_int8(1, 64, 56, 64, 3, 1, 1, add_bias=True) verify_conv2d_NCHWc_int8(1, 64, 56, 64, 3, 1, 1, add_bias=True, add_relu=True) # dilation = 2 verify_conv2d_NCHWc_int8(1, 64, 56, 64, 3, 1, 1, dilation=2) # batch size verify_conv2d_NCHWc_int8(4, 64, 56, 64, 3, 1, 1) verify_conv2d_NCHWc_int8(9, 64, 56, 64, 3, 1, 1) # weird workloads verify_conv2d_NCHWc_int8(4, 4, 4, 4, 4, 4, 4) # inception v3 workloads where channels in / out are multiple of oc_block_factor verify_conv2d_NCHWc_int8(1, 32, 149, 32, 3, 1, 0) verify_conv2d_NCHWc_int8(1, 32, 147, 64, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 64, 73, 80, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 80, 73, 192, 3, 1, 0) verify_conv2d_NCHWc_int8(1, 192, 35, 64, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 192, 35, 48, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 48, 35, 64, 5, 1, 2) verify_conv2d_NCHWc_int8(1, 64, 35, 96, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 96, 35, 96, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 192, 35, 32, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 256, 35, 64, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 256, 35, 48, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 288, 35, 64, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 288, 35, 48, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 288, 35, 384, 3, 2, 0) verify_conv2d_NCHWc_int8(1, 96, 35, 96, 3, 2, 0) verify_conv2d_NCHWc_int8(1, 768, 17, 192, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 768, 17, 128, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 128, 17, 128, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 128, 17, 192, 7, 1, 3) verify_conv2d_NCHWc_int8(1, 128, 17, 128, 7, 1, 3) verify_conv2d_NCHWc_int8(1, 128, 17, 192, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 768, 17, 160, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 160, 17, 160, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 160, 17, 192, 7, 1, 3) verify_conv2d_NCHWc_int8(1, 160, 17, 160, 7, 1, 3) verify_conv2d_NCHWc_int8(1, 160, 17, 192, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 192, 17, 192, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 192, 17, 192, 7, 1, 3) verify_conv2d_NCHWc_int8(1, 192, 17, 320, 3, 2, 0) verify_conv2d_NCHWc_int8(1, 192, 17, 192, 3, 2, 0) verify_conv2d_NCHWc_int8(1, 1280, 8, 320, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 1280, 8, 384, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 384, 8, 384, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 384, 8, 384, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 1280, 8, 448, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 448, 8, 384, 3, 1, 1) verify_conv2d_NCHWc_int8(1, 1280, 8, 192, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 2048, 8, 320, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 2048, 8, 384, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 2048, 8, 448, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 2048, 8, 192, 1, 1, 0) verify_conv2d_NCHWc_int8(1, 1024, 19, 84, 3, 1, 1) # batch > 1 verify_conv2d_NCHWc_int8(7, 32, 149, 32, 3, 1, 0) verify_conv2d_NCHWc_int8(8, 32, 149, 32, 3, 1, 0) verify_conv2d_NCHWc_int8(32, 32, 149, 32, 3, 1, 0) # Asymmetric padding verify_conv2d_NCHWc_int8(1, 32, 35, 64, 7, 2, (0, 0, 1, 1)) verify_conv2d_NCHWc_int8(1, 64, 8, 128, 3, 1, (3, 3, 2, 2)) verify_conv2d_NCHWc_int8(1, 64, 8, 64, 1, 1, (1, 2, 2, 1)) verify_conv2d_NCHWc_int8(1, 64, 17, 192, 1, 1, (1, 2)) verify_conv2d_NCHWc_int8(1, 64, 8, 64, 3, 1, (3, 1)) verify_conv2d_NCHWc_int8(1, 128, 8, 384, 3, 1, (0, 2)) verify_conv2d_NCHWc_int8(1, 64, 8, 64, 1, 1, "VALID") verify_conv2d_NCHWc_int8(1, 388, 8, 64, 3, 1, "VALID") verify_conv2d_NCHWc_int8(1, 512, 19, 64, 1, 1, "SAME") verify_conv2d_NCHWc_int8(1, 64, 16, 32, 2, 1, "SAME") verify_conv2d_NCHWc_int8(1, 64, 8, 64, 3, 1, (1, 2, 2, 1), add_relu=True) verify_conv2d_NCHWc_int8(1, 64, 8, 64, 5, 2, (1, 3), add_bias=True) verify_conv2d_NCHWc_int8(1, 64, 56, 64, 3, 1, "VALID", add_bias=True, add_relu=True) verify_conv2d_NCHWc_int8(1, 64, 56, 64, 24, 1, "SAME", add_bias=True, add_relu=True) # Conv2d NCHW int8 schedule testing. Internally, it uses NCHWc schedule. So, just # performing basic testing - one test for all different scenarios - batch, dilation etc.. verify_conv2d_nchw_int8(1, 64, 56, 64, 3, 1, 1) verify_conv2d_nchw_int8(1, 64, 56, 64, 3, 1, 1, add_relu=True) verify_conv2d_nchw_int8(1, 64, 56, 64, 3, 1, 1, dilation=2) verify_conv2d_nchw_int8(9, 64, 56, 64, 3, 1, 1) verify_conv2d_nchw_int8(4, 4, 4, 4, 4, 4, 4) verify_conv2d_nchw_int8(1, 32, 149, 32, 3, 1, 0) verify_conv2d_nchw_int8(7, 32, 149, 32, 3, 1, 0) verify_conv2d_nchw_int8(1, 32, 35, 64, 7, 2, (0, 0, 1, 1)) if __name__ == "__main__": test_conv2d_nchw()