masahi commented on a change in pull request #10112:
URL: https://github.com/apache/tvm/pull/10112#discussion_r814241095
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File path: tests/python/relay/test_cpp_build_module.py
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@@ -93,6 +93,35 @@ def test_fp16_build():
np.testing.assert_allclose(out.numpy(), X.numpy() + Y.numpy(), atol=1e-5,
rtol=1e-5)
[email protected]_llvm
+def test_bf16_build():
+ data = relay.var("data", shape=(1, 3, 224, 224), dtype='float32')
+ weight = relay.var("weight", shape=(64, 3, 7, 7), dtype='float32')
+ bn_gamma = relay.var("gamma", shape=(64,), dtype='float32')
+ bn_beta = relay.var("beta", shape=(64,), dtype='float32')
+ bn_mean = relay.var("mean", shape=(64,), dtype='float32')
+ bn_var = relay.var("var", shape=(64,), dtype='float32')
+ params = {
+ "weight": np.random.uniform(-1, 1, size=(64, 3, 7,
7)).astype('float32'),
+ "gamma": np.random.uniform(-1, 1, size=(64, )).astype('float32'),
+ "beta": np.random.uniform(-1, 1, size=(64, )).astype('float32'),
+ "mean": np.random.uniform(-1, 1, size=(64, )).astype('float32'),
+ "var": np.random.uniform(-1, 1, size=(64, )).astype('float32'),
+ }
+ conv_bf16 = relay.nn.conv2d(relay.cast(data, 'bfloat16'),
relay.cast(weight, 'bfloat16'),
+ strides=(2, 2), padding=(3, 3, 3, 3),
channels=64, kernel_size=(7, 7), out_dtype='bfloat16')
+ bn_bf16 = relay.nn.batch_norm(conv_bf16, relay.cast(bn_gamma, 'bfloat16'),
+ relay.cast(bn_beta, 'bfloat16'),
relay.cast(bn_mean, 'bfloat16'), relay.cast(bn_var, 'bfloat16'))
+ relu_bf16 = relay.nn.relu(bn_bf16[0])
+ maxpool_bf16 = relay.nn.max_pool2d(
+ relu_bf16, pool_size=(2, 2), strides=(2, 2))
+ avgpool_bf16 = relay.nn.avg_pool2d(
+ maxpool_bf16, pool_size=(2, 2), strides=(2, 2))
+ mod_bf16 = tvm.IRModule.from_expr(avgpool_bf16)
+ with tvm.transform.PassContext(opt_level=3):
+ relay.build(mod_bf16, target="llvm", params=params)
Review comment:
Yeah, even fp16 is not trivial for accuracy checking. I can imagine how
hard bfloat is too.
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