[TOPI] Support int4/int8 conv2d tensor core with HWNC layout

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

@@ -172,6 +172,25 @@ def conv2d_strategy_cuda(attrs, inputs, out_type, target):
                             wrap_topi_schedule(topi.cuda.schedule_conv2d_nhwc_tensorcore),
                             name="conv2d_nhwc_tensorcore.cuda",
                             plevel=20)
+        elif layout == "HWNC":
+            assert kernel_layout in ["HWOI", "HWOI16o16i", "HWOI8o32i", "HWOI32o16i"]
+            _, _, N, in_channels = get_const_tuple(data.shape)
+            pre_computed = len(kernel.shape) == 6
+            if pre_computed:
+                _, _, oc_chunk, _, oc_block_factor, _ = get_const_tuple(kernel.shape)
+                out_channels = oc_chunk * oc_block_factor
+            else:
+                _, _, out_channels, _ = get_const_tuple(kernel.shape)
+            if topi.cuda.is_shape_tensorcore_direct_qualified(
+                    batch=N, in_channels=in_channels, num_filter=out_channels, in_dtype=data.dtype):
+                strategy.add_implementation(
+                    wrap_compute_conv2d(topi.cuda.conv2d_hwnc_tensorcore),
+                    wrap_topi_schedule(topi.cuda.schedule_conv2d_hwnc_tensorcore),
+                    name="conv2d_hwnc_tensorcore_direct.cuda",
+                    plevel=20)
+            else:
+                raise RuntimeError("Unsupported shape for conv2d HWNC.\
+                                    Need to satisfy tensor core schedule.")
         elif layout == "NCHW4c" and data.dtype in ["int8", "uint8"]:
             assert kernel_layout == "OIHW4o4i"
             strategy.add_implementation(

python/tvm/topi/cuda/__init__.py

@@ -50,5 +50,6 @@
 from .conv2d_nhwc_tensorcore import *
 from .conv3d_ndhwc_tensorcore import *
 from .dense_tensorcore import *
+from .conv2d_hwnc_tensorcore import *
 from .correlation import *
 from .sparse import *

python/tvm/topi/cuda/conv2d_alter_op.py

@@ -171,6 +171,36 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         dispatch_ctx.update(target, new_workload, cfg)
         return relay.nn.conv2d(*inputs, **new_attrs)
 
+    if topi_tmpl == "conv2d_HWNCnc_tensorcore.cuda":
+        assert data_layout == "HWNC" and kernel_layout == "HWOI"
+        assert float(tvm.gpu(0).compute_version) >= 7.5
+        H, W, N, CI = get_const_tuple(data.shape)
+        KH, KW, CO, _ = get_const_tuple(kernel.shape)
+
+        if kernel.dtype in ['int4', 'uint4'] and (CI % 32 != 0 or CO % 8 != 0) or \
+            kernel.dtype in ['int8', 'uint8'] and (CI % 16 != 0 or CO % 32 != 0):
+            return relay.nn.conv2d(*inputs, **new_attrs)
+
+        new_attrs["channels"] = CO
+        if kernel.dtype in ['int4', 'uint4']:
+            new_attrs['kernel_layout'] = 'HWOI8o32i'
+            ic_block_factor = 32
+            oc_block_factor = 8
+        else:
+            new_attrs['kernel_layout'] = 'HWOI32o16i'
+            ic_block_factor = 16
+            oc_block_factor = 32
+
+        new_kernel = te.placeholder((KH, KW, CO // oc_block_factor, CI // ic_block_factor,
+                                     oc_block_factor, ic_block_factor), dtype=kernel.dtype)
+
+        new_workload = autotvm.task.args_to_workload(
+            [data, new_kernel, strides, padding, dilation, out_dtype],
+            "conv2d_HWNCnc_tensorcore.cuda")
+
+        dispatch_ctx.update(target, new_workload, cfg)
+        return relay.nn.conv2d(*inputs, **new_attrs)
+
     return None
 
 @conv2d_legalize.register("cuda")