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

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Laurawly 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_r396038441
 
 

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 File path: topi/tests/python/test_topi_dense_tensorcore.py
 ##########
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+# 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
+"""Test code for dense tensorcore operator"""
+import numpy as np
+import tvm
+import topi
+import topi.testing
+from topi.util import get_const_tuple
+from tvm import te
+from tvm.contrib.pickle_memoize import memoize
+from tvm.contrib import nvcc
+
+
+_dense_implement = {
+    "gpu": [(topi.cuda.dense_tensorcore, topi.cuda.schedule_dense_tensorcore)]
+}
+
+def verify_dense(batch, in_dim, out_dim, use_bias=True):
+    """Dense tensorcore verify function"""
+    A = te.placeholder((batch, in_dim), name='A')
+    B = te.placeholder((out_dim, in_dim), name='B')
+    C = te.placeholder((out_dim,), name='C')
+    dtype = A.dtype
+
+    # use memoize to pickle the test data for next time use
+    @memoize("topi.tests.test_topi_dense_tensorcore")
+    def get_ref_data():
+        a_np = np.random.uniform(size=(batch, in_dim)).astype(dtype)
+        b_np = np.random.uniform(size=(out_dim, in_dim)).astype(dtype)
+        c_np = np.random.uniform(size=(out_dim,)).astype(dtype)
+        if use_bias:
+            d_np = np.maximum(np.dot(a_np, b_np.T) + c_np, 0.0)
+        else:
+            d_np = np.maximum(np.dot(a_np, b_np.T), 0.0)
+        return (a_np, b_np, c_np, d_np)
+    # get the test data
+    a_np, b_np, c_np, d_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)
+        for fcompute, fschedule in topi.testing.dispatch(device, _dense_implement):
+            with tvm.target.create(device):
+                D = fcompute(A, B, C if use_bias else None)
+                D = topi.nn.relu(D)
+                s = fschedule([D])
+            a = tvm.nd.array(a_np, ctx)
+            b = tvm.nd.array(b_np, ctx)
+            c = tvm.nd.array(c_np, ctx)
+            d = tvm.nd.array(np.zeros(get_const_tuple(D.shape), dtype=dtype), ctx)
+            f = tvm.build(s, [A, B, C, D], device, name="dense")
+            f(a, b, c, d)
+            tvm.testing.assert_allclose(d.asnumpy(), d_np, rtol=1e-3)
+
+
+    for device in ['cuda']:
+        check_device(device)
+
+
+def test_dense_tensorcore():
+    """Test cases"""
+    verify_dense(8, 16, 32, use_bias=True)
 
 Review comment:
   Is there any shape limitation for dense?

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