[GitHub] [incubator-tvm] Shawn-Inspur commented on a change in pull request #5099: [TOPI][Tensor Core] Conv2d and Dense ops support on Tensor Core

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Shawn-Inspur 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_r396376850
 
 

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 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})
 
 Review comment:
   Thanks for the suggestions! The codes has been modified to have a general gemm intrinsic for both conv2d and dense.

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