[Topi] Tensorcore support for Conv3D (#5284)

* one weird trick.

* Added schedule knob for different workloads.

* Initial conv3d tensorcore working.

* Added conv3d tensorcore strategy.

* Added layout conversion to tensorcore friendly format for conv2d and conv3d.

* Add target name check.

* Fixed bad names and depthwise check.

* Removed duplicated attribute assignment.

python/tvm/relay/op/nn/_nn.py

@@ -27,6 +27,7 @@ from .. import op as reg
 from .. import strategy
 from ..op import OpPattern
 from .._tensor import elemwise_shape_func
+from ..strategy.generic import is_depthwise_conv2d
 
 # relu
 reg.register_broadcast_schedule("nn.relu")
@@ -139,13 +140,21 @@ def convert_conv2d(attrs, inputs, tinfos, desired_layout):
     # pylint: disable=import-outside-toplevel
     from tvm import relay
     data, weight = inputs
-    assert desired_layout == 'NCHW', \
-            "Currently only transformation to NCHW layout is supported."
-    if desired_layout == 'NCHW':
     new_attrs = dict(attrs)
     new_attrs['data_layout'] = desired_layout
+    if desired_layout == 'NCHW':
         new_attrs['kernel_layout'] = 'OIHW'
         return relay.nn.conv2d(data, weight, **new_attrs)
+    elif desired_layout == 'NHWC':
+        # Check for depthwise convolution.
+        if is_depthwise_conv2d(data.shape, attrs['data_layout'], weight.shape,
+                               attrs['kernel_layout'], attrs['groups']):
+            new_attrs['kernel_layout'] = 'HWOI'
+        else:
+            new_attrs['kernel_layout'] = 'HWIO'
+        return relay.nn.conv2d(data, weight, **new_attrs)
+    else:
+        assert "Layout %s is not yet supported." % (desired_layout)
     return None
 
 
@@ -183,6 +192,41 @@ def alter_op_layout_conv3d(attrs, inputs, tinfos, out_type):
     """Alternate the layout of conv3d"""
     return topi.nn.conv3d_alter_layout(attrs, inputs, tinfos, out_type)
 
+@reg.register_convert_op_layout("nn.conv3d")
+def convert_conv3d(attrs, inputs, tinfos, desired_layout):
+    """Convert Layout pass registration for conv3d op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    tinfos : list of types
+        List of input and output types
+    desired_layout : str
+        The desired layout
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The transformed expr
+    """
+    # pylint: disable=import-outside-toplevel
+    from tvm import relay
+    data, weight = inputs
+    new_attrs = dict(attrs)
+    new_attrs['data_layout'] = desired_layout
+    if desired_layout == 'NCDHW':
+        new_attrs['kernel_layout'] = 'OIDHW'
+        return relay.nn.conv3d(data, weight, **new_attrs)
+    elif desired_layout == "NDHWC":
+        new_attrs['kernel_layout'] = 'DHWIO'
+        return relay.nn.conv3d(data, weight, **new_attrs)
+    else:
+        assert "Layout %s is not yet supported" % desired_layout
+    return None
+
 # conv3d_winograd related operators
 reg.register_strategy("nn.contrib_conv3d_winograd_without_weight_transform",
                       strategy.conv3d_winograd_without_weight_transfrom_strategy)

python/tvm/relay/op/strategy/cuda.py

@@ -138,6 +138,7 @@ def conv2d_strategy_cuda(attrs, inputs, out_type, target):
                 name="conv2d_nhwc.cuda")
             N, _, _, _ = get_const_tuple(data.shape)
             _, _, CI, CO = get_const_tuple(kernel.shape)
+            if target.target_name == "cuda":
                 if nvcc.have_tensorcore(tvm.gpu(0).compute_version):
                     if (N % 16 == 0 and CI % 16 == 0 and CO % 16 == 0) or \
                             (N % 8 == 0 and CI % 16 == 0 and CO % 32 == 0) or \
@@ -170,7 +171,7 @@ def conv2d_strategy_cuda(attrs, inputs, out_type, target):
             strategy.add_implementation(
                 wrap_compute_conv2d(topi.cuda.depthwise_conv2d_nchw),
                 wrap_topi_schedule(topi.cuda.schedule_depthwise_conv2d_nchw),
-                name="dpethwise_nchw.cuda")
+                name="depthwise_conv2d_nchw.cuda")
         elif layout == "NHWC":
             assert kernel_layout == "HWOI"
             strategy.add_implementation(
@@ -249,7 +250,7 @@ def conv2d_transpose_strategy_cuda(attrs, inputs, out_type, target):
 def conv3d_strategy_cuda(attrs, inputs, out_type, target):
     """conv3d cuda strategy"""
     strategy = _op.OpStrategy()
-    _, kernel = inputs
+    data, kernel = inputs
     layout = attrs.data_layout
     _, stride_h, stride_w = attrs.get_int_tuple("strides")
     _, dilation_h, dilation_w = attrs.get_int_tuple("dilation")
@@ -269,10 +270,24 @@ def conv3d_strategy_cuda(attrs, inputs, out_type, target):
                 name="conv3d_ncdhw_winograd.cuda",
                 plevel=5)
     else:  # layout == "NDHWC":
-        strategy.add_implementation(wrap_compute_conv3d(topi.cuda.conv3d_ndhwc),
+        strategy.add_implementation(
+            wrap_compute_conv3d(topi.cuda.conv3d_ndhwc),
             wrap_topi_schedule(topi.cuda.schedule_conv3d_ndhwc),
             name="conv3d_ndhwc.cuda",
             plevel=10)
+        N, _, _, _, _ = get_const_tuple(data.shape)
+        _, _, _, CI, CO = get_const_tuple(kernel.shape)
+        if target.target_name == "cuda":
+            if nvcc.have_tensorcore(tvm.gpu(0).compute_version):
+                if (N % 16 == 0 and CI % 16 == 0 and CO % 16 == 0) or \
+                (N % 8 == 0 and CI % 16 == 0 and CO % 32 == 0) or \
+                (N % 32 == 0 and CI % 16 == 0 and CO % 8 == 0):
+                    strategy.add_implementation(
+                        wrap_compute_conv3d(topi.cuda.conv3d_ndhwc_tensorcore),
+                        wrap_topi_schedule(topi.cuda.schedule_conv3d_ndhwc_tensorcore),
+                        name="conv3d_ndhwc_tensorcore.cuda",
+                        plevel=20)
+
     if target.target_name == "cuda" and "cudnn" in target.libs:
         strategy.add_implementation(wrap_compute_conv3d(topi.cuda.conv3d_cudnn, True),
                                     wrap_topi_schedule(topi.cuda.schedule_conv3d_cudnn),

topi/python/topi/cuda/__init__.py

@@ -46,4 +46,5 @@ from .nms import get_valid_counts, non_max_suppression
 from .rcnn import *
 from .sort import *
 from .conv2d_nhwc_tensorcore import *
+from .conv3d_ndhwc_tensorcore import *
 from .dense_tensorcore import *

topi/python/topi/cuda/conv2d_nhwc_tensorcore.py

@@ -70,7 +70,7 @@ def nhwc_tensorcore_cuda(cfg, Input, Filter, stride, padding, dilation, out_dtyp
     # convert data type of input feature maps and weights
     TransPaddedInput = te.compute(
         PaddedInput.shape,
-        lambda h, w, i, o: PaddedInput[h, w, i, o].astype('float16'))
+        lambda n, h, w, c: PaddedInput[n, h, w, c].astype('float16'))
     TransFilter = te.compute(
         Filter.shape, lambda h, w, i, o: Filter[h, w, i, o].astype('float16'))
     Output = te.compute(

topi/python/topi/cuda/conv3d_winograd.py

@@ -493,12 +493,16 @@ def schedule_winograd_no_depth_cuda(cfg, s, output, pre_computed):
     BB = s.cache_read(B0, 'shared', [OL])
 
     b = s[bgemm].fuse(b1, b2)
-    y = s[bgemm].fuse(z, y)
-
+    # Allow two different tiling strategies as both seem
+    # to work best in different cases.
+    cfg.define_knob("unroll_axis", [0, 1])
     # tile and bind spatial axes
     bgemm_scope, b = s[bgemm].split(b, nparts=1)
     bz, vz, tz, zi = cfg["tile_b"].apply(s, C, b)
-    by, vy, ty, yi = cfg["tile_y"].apply(s, C, y)
+    by, vy, ty, yi = cfg["tile_y"].apply(s, C, z)
+    if cfg['unroll_axis'].val:
+        bx, vx, tx, xi = cfg["tile_x"].apply(s, C, y)
+    else:
         bx, vx, tx, xi = cfg["tile_x"].apply(s, C, x)
     s[C].bind(bz, te.thread_axis("blockIdx.z"))
     s[C].bind(by, te.thread_axis("blockIdx.y"))
@@ -510,6 +514,10 @@ def schedule_winograd_no_depth_cuda(cfg, s, output, pre_computed):
     s[C].bind(ty, te.thread_axis("threadIdx.y"))
     s[C].bind(tx, te.thread_axis("threadIdx.x"))
     s[C].reorder(bgemm_scope, bz, by, bx, vz, vy, vx, tz, ty, tx, zi, yi, xi)
+    if cfg['unroll_axis'].val:
+        s[C].unroll(x)
+    else:
+        s[C].unroll(y)
 
     # tile reduction axes
     s[OL].compute_at(s[C], tx)

topi/tests/python/test_topi_conv3d_ndhwc_tensorcore.py

+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name, too-many-locals, too-many-arguments
+"""Example code to do convolution."""
+
+import numpy as np
+import tvm
+import topi
+import topi.testing
+from tvm import te
+from tvm.contrib.pickle_memoize import memoize
+from tvm.contrib import nvcc
+from topi.nn.util import get_pad_tuple3d
+from topi.util import get_const_tuple
+
+
+_conv3d_ndhwc_tensorcore_implement = {
+    "cuda": (topi.cuda.conv3d_ndhwc_tensorcore, topi.cuda.schedule_conv3d_ndhwc_tensorcore)
+}
+
+
+def verify_conv3d_ndhwc(batch, in_channel, in_size, num_filter, kernel, stride,
+                        padding, dilation=1, add_bias=False, add_relu=False, devices='cuda'):
+    """Test the conv3d with tensorcore for ndhwc layout"""
+    pad_front, pad_top, pad_left, pad_back, pad_bottom, pad_right = get_pad_tuple3d(
+        padding, (kernel, kernel, kernel))
+    padding_sum = pad_front + pad_top + pad_left + pad_back + 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_depth = in_height = in_width = in_size
+
+    A = te.placeholder((batch, in_depth, in_height, in_width, in_channel), name='A')
+    W = te.placeholder((kernel, kernel, kernel, in_channel, num_filter), name='W')
+    bias = te.placeholder((1, 1, 1, 1, num_filter), name='bias')
+
+    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_conv3d_ndhwc.verify_conv3d_ndhwc")
+    def get_ref_data():
+        a_np = np.random.uniform(size=a_shape).astype(dtype)
+        w_np = np.random.uniform(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, 1, dilation, dilation))
+        c_np = topi.testing.conv3d_ndhwc_python(a_np, dw_np, stride, padding)
+        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 not nvcc.have_tensorcore(ctx.compute_version):
+            print("skip because gpu does not support Tensor Cores")
+            return
+        print("Running on target: %s" % device)
+        with tvm.target.create(device):
+            fcompute, fschedule = topi.testing.dispatch(device, _conv3d_ndhwc_tensorcore_implement)
+            C = fcompute(A, W, stride, padding, dilation, 'float32')
+            if add_bias:
+                C = topi.add(C, bias)
+            if add_relu:
+                C = topi.nn.relu(C)
+            s = fschedule([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)
+
+        rtol = 1e-3
+        tvm.testing.assert_allclose(c.asnumpy(), c_np, rtol=rtol)
+
+    check_device(devices)
+
+
+def test_conv3d_ndhwc_tensorcore():
+    """Test the conv3d with tensorcore for ndhwc layout"""
+    verify_conv3d_ndhwc(16, 16, 14, 16, 3, 1, 1)
+    verify_conv3d_ndhwc(16, 64, 7, 64, 7, 1, 3)
+    verify_conv3d_ndhwc(16, 32, 7, 32, 7, 1, 3)
+
+    verify_conv3d_ndhwc(32, 16, 14, 16, 3, 1, 1, add_bias=True)
+    verify_conv3d_ndhwc(32, 16, 14, 16, 3, 1, 1, add_relu=True)
+    verify_conv3d_ndhwc(32, 16, 14, 16, 3, 1, 1, add_relu=True, add_bias=True)
+
+    verify_conv3d_ndhwc(16, 16, 17, 16, 7, 1, (3, 3, 3, 2, 2, 2))
+    verify_conv3d_ndhwc(16, 16, 17, 16, 7, 1, "SAME")
+    verify_conv3d_ndhwc(8, 16, 35, 32, 5, 1, "VALID")
+    verify_conv3d_ndhwc(16, 32, 16, 32, 3, 1, (1, 1, 1, 1, 1, 1))
+    verify_conv3d_ndhwc(16, 16, 12, 16, 3, 1, (1, 1, 1, 1, 1, 1))
+
+
+if __name__ == "__main__":
+    test_conv3d_ndhwc_tensorcore()