jwfromm commented on a change in pull request #5099: [TOPI][Tensor Core] Conv2d 
and Dense ops support on Tensor Core
URL: https://github.com/apache/incubator-tvm/pull/5099#discussion_r395912368
 
 

 ##########
 File path: topi/python/topi/cuda/conv2d_nhwc_tensorcore.py
 ##########
 @@ -0,0 +1,361 @@
+# 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
+# pylint: disable=too-many-statements, unused-argument
+"""Tensorcore template for cuda backend"""
+import numpy as np
+import tvm
+from tvm import te
+from tvm import autotvm
+from ..util import get_const_tuple, traverse_inline, simplify
+from ..nn.pad import pad
+from ..nn.util import get_pad_tuple
+from .tensor_intrin import intrin_wmma_load_matrix_A
+from .tensor_intrin import intrin_wmma_load_matrix_W
+from .tensor_intrin import intrin_wmma_store_matrix
+
+
+def intrin_wmma_gemm(strides_A, strides_W, strides_Conv, shape, out_dtype):
+    """Intrin for wmma fill_fragment and mma_sync"""
+    wmma_m, wmma_n, wmma_k = shape
+    A = te.placeholder((wmma_m, 1, 1, wmma_k), name='A', dtype='float16')
+    B = te.placeholder((wmma_k, wmma_n), name='B', dtype='float16')
+    k = te.reduce_axis((0, wmma_k), name="k")
+    C = te.compute((wmma_m, 1, 1, wmma_n),
+                   lambda ii, t0, t1, jj:
+                   te.sum(A[ii, t0, t1, k].astype(out_dtype) * \
+                          B[k, jj].astype(out_dtype), axis=k),
+                   name='C')
+    BA = tvm.tir.decl_buffer(A.shape, A.dtype, name='BA',
+                             scope='wmma.matrix_a', data_alignment=32,
+                             offset_factor=8, strides=strides_A)
+    BB = tvm.tir.decl_buffer(B.shape, B.dtype, name='BB',
+                             scope='wmma.matrix_b', data_alignment=32,
+                             offset_factor=8, strides=strides_W)
+    BC = tvm.tir.decl_buffer(C.shape, C.dtype, name='BC',
+                             scope='wmma.accumulator', data_alignment=32,
+                             offset_factor=8, strides=strides_Conv)
+
+    def intrin_func(ins, outs):
+        BA, BB = ins
+        BC, = outs
+
+        def warp_idnex(offset, row, col):
+            row = row * col
+            return offset // row + offset % row // col
+
+        warp_index_A = warp_idnex(BA.elem_offset, wmma_m, wmma_k)
+        warp_index_B = warp_idnex(BB.elem_offset, wmma_k, wmma_n)
+        warp_index_C = warp_idnex(BC.elem_offset, wmma_m, wmma_n)
+
+        def init():
+            ib = tvm.tir.ir_builder.create()
+            ib.emit(
+                tvm.tir.call_intrin('handle', 'tvm_fill_fragment', BC.data, 
wmma_m, wmma_n, wmma_k,
+                                    warp_index_C, 0.0))
+            return ib.get()
+
+        def update():
+            ib = tvm.tir.ir_builder.create()
+            ib.emit(tvm.tir.call_intrin('handle', 'tvm_mma_sync',
+                                        BC.data, warp_index_C,
+                                        BA.data, warp_index_A,
+                                        BB.data, warp_index_B,
+                                        BC.data, warp_index_C))
+            return ib.get()
+
+        return update(), init(), update()
+
+    return te.decl_tensor_intrin(C.op, intrin_func, binds={A: BA, B: BB, C: 
BC})
+
+
+def nhwc_tensorcore_cuda(cfg, Input, Filter, stride, padding, dilation, 
out_dtype):
+    """Compute declaration for tensorcore"""
+    assert isinstance(stride, int) or len(stride) == 2
+    assert isinstance(dilation, int) or len(dilation) == 2
+
+    if isinstance(stride, int):
+        stride_h = stride_w = stride
+    else:
+        stride_h, stride_w = stride
+
+    if isinstance(dilation, int):
+        dilation_h = dilation_w = dilation
+    else:
+        dilation_h, dilation_w = dilation
+
+    batch, in_height, in_width, in_channel = Input.shape
+    kernel_h, kernel_w, _, num_filter = Filter.shape
+    # compute the output shape
+    dilated_kernel_h = (kernel_h - 1) * dilation_h + 1
+    dilated_kernel_w = (kernel_w - 1) * dilation_w + 1
+    pad_top, pad_left, pad_down, pad_right = get_pad_tuple(
+        padding, (dilated_kernel_h, dilated_kernel_w))
+    out_channel = num_filter
+    out_height = simplify((in_height - dilated_kernel_h + pad_top + pad_down) 
// stride_h + 1)
+    out_width = simplify((in_width - dilated_kernel_w + pad_left + pad_right) 
// stride_w + 1)
+    pad_before = [0, pad_top, pad_left, 0]
+    pad_after = [0, pad_down, pad_right, 0]
+    PaddedInput = pad(Input, pad_before, pad_after, name="PaddedInput")
+    rc = te.reduce_axis((0, in_channel), name='rc')
+    ry = te.reduce_axis((0, kernel_h), name='ry')
+    rx = te.reduce_axis((0, kernel_w), name='rx')
+    # 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'))
+    TransFilter = te.compute(
+        Filter.shape, lambda h, w, i, o: Filter[h, w, i, o].astype('float16'))
 
 Review comment:
   Does it make sense to do this casting as part of the function? Instead we 
could apply a relay pass to downcast to `float16` ahead of time, which should 
be more efficient and make the conversions more visible to users.

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