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Subject [GitHub] [incubator-tvm] Shawn-Inspur commented on a change in pull request #5099: [TOPI][Tensor Core] Conv2d and Dense ops support on Tensor Core
Date Mon, 23 Mar 2020 11:31:10 GMT
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_r396384057
 
 

 ##########
 File path: topi/python/topi/cuda/dense_tensorcore.py
 ##########
 @@ -0,0 +1,290 @@
+# 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-statements, unused-argument
+"""Compute and Schedule definition for dense tensorcore with cuda backend"""
+from __future__ import absolute_import as _abs
+import tvm
+from tvm import te
+import tvm.autotvm as autotvm
+from .. import tag
+from ..util import traverse_inline, get_const_tuple
+from .tensor_intrin import intrin_wmma_load_matrix_A, \
+        intrin_wmma_load_matrix_W, intrin_wmma_store_matrix
+
+
+@autotvm.register_topi_compute("dense_tensorcore.cuda")
+def dense_tensorcore(cfg, data, weight, bias=None, out_dtype=None):
+    """Dense tensorcore operator on CUDA"""
+    matmul = dense_tensorcore_cuda(data, weight, bias, out_dtype)
+    return matmul
+
+
+@autotvm.register_topi_schedule("dense_tensorcore.cuda")
+def schedule_dense_tensorcore(cfg, outs):
+    """Schedule dense operator using Tensorcore"""
+    outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+    s = te.create_schedule([x.op for x in outs])
+
+    def _callback(op):
+        if op.tag == 'dense_tensorcore':
+            _schedule_dense_tensorcore(cfg, s, op.output(0))
+    traverse_inline(s, outs[0].op, _callback)
+    return s
+
+
+def dense_tensorcore_cuda(data, weight, bias=None, out_dtype=None):
+    """Dense tensorcore operator on CUDA"""
+    assert len(data.shape) == 2 and len(weight.shape) == 2, \
+        "only support 2-dim dense"
+    if bias is not None:
+        assert len(bias.shape) == 1
+    if out_dtype is None:
+        out_dtype = data.dtype
+    batch, in_dim = data.shape
+    out_dim, _ = weight.shape
+    k = te.reduce_axis((0, in_dim), name='k')
+    data_16 = te.compute((batch, in_dim), lambda b, i: data[b, i].astype('float16'))
+    weight_16 = te.compute((out_dim, in_dim), lambda o, i: weight[o, i].astype('float16'))
+    matmul = te.compute((batch, out_dim), \
+                         lambda i, j: te.sum(data_16[i, k].astype(out_dtype) * \
+                                              weight_16[j, k].astype(out_dtype), axis=k),
\
+                         name='T_dense', tag='dense_tensorcore')
+    if bias is not None:
+        matmul = te.compute((batch, out_dim), \
+                             lambda i, j: matmul[i, j] + bias[j].astype(out_dtype), \
+                             tag=tag.BROADCAST)
+    return matmul
+
+
+def _schedule_dense_tensorcore(cfg, s, C):
+    """Schedule dense operator using Tensorcore"""
+    A, B = s[C].op.input_tensors
+    batch, out_dim = get_const_tuple(C.shape)
+    out_dtype = C.dtype
+    s[A].compute_inline()
+    s[B].compute_inline()
+
+    # Explicit memory access
+    AS = s.cache_read(A, 'shared', [C])
+    BS = s.cache_read(B, 'shared', [C])
+    AF = s.cache_read(AS, 'wmma.matrix_a', [C])
+    BF = s.cache_read(BS, 'wmma.matrix_b', [C])
+    CF = s.cache_write(C, 'wmma.accumulator')
+    CS = s.cache_read(CF, 'shared', [C])
+
+    # fallback support
+    target = tvm.target.Target.current()
+    if cfg.is_fallback:
+        ref_log = autotvm.tophub.load_reference_log(
+            target.target_name, target.model, 'dense_tensorcore.cuda')
+        cfg.fallback_with_reference_log(ref_log)
+
+    # Deal with op fusion, such as bias and relu
+    if C.op not in s.outputs:
+        s[C].compute_inline()
+        C = s.outputs[0].output(0)
+
+    # create tuning space
+    cfg.define_knob("block_row_warps", [1, 2, 4])
+    cfg.define_knob("block_col_warps", [1, 2, 4])
+    cfg.define_knob("warp_row_tiles", [1, 2, 4])
+    cfg.define_knob("warp_col_tiles", [1, 2, 4])
+    cfg.define_knob("chunk", [1, 2, 4, 8])
+    cfg.define_knob("offset", [0, 8])
+    cfg.define_knob("offsetCS", [0, 8])
+    cfg.define_knob("vec", [1, 2, 4, 8])
+
+    #Make it available by default
 
 Review comment:
   It means the default parameters are applicable when users do not take autoTVM to fine tune
the parameters. We have modified this comment to make it clear.

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