crazydemo commented on code in PR #12151:
URL: https://github.com/apache/tvm/pull/12151#discussion_r932899385
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -788,6 +789,44 @@ def test_conv2d_pattern(run_module, dtype="float32"):
run_and_verify_func(config, run_module=run_module, dtype=dtype)
+def test_conv2d_bias_sum_relu(run_module, dtype="float32"):
+ x_shape = (1, 32, 8, 8)
+ k_shape = (16, 32, 3, 3)
+
+ def get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape, dtype="float32"):
+ out, dic, param_lst = get_conv2d_bias(x_shape=x_shape,
k_shape=k_shape, dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ out, _, _ = relay.nn.batch_norm(
+ out,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+ out = relay.add(out, sum_data)
+ dic["data1"] = sum_shape
+ param_lst += ["data1"]
+ return relay.nn.relu(out), dic, param_lst
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape,
k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype)
Review Comment:
Fixed.
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -788,6 +789,44 @@ def test_conv2d_pattern(run_module, dtype="float32"):
run_and_verify_func(config, run_module=run_module, dtype=dtype)
+def test_conv2d_bias_sum_relu(run_module, dtype="float32"):
+ x_shape = (1, 32, 8, 8)
+ k_shape = (16, 32, 3, 3)
+
+ def get_conv2d_bn_sum_relu(x_shape, k_shape, sum_shape, dtype="float32"):
+ out, dic, param_lst = get_conv2d_bias(x_shape=x_shape,
k_shape=k_shape, dtype=dtype)
+ beta = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ gamma = relay.const(np.ones(k_shape[0]).astype(dtype))
+ moving_mean = relay.const(np.zeros(k_shape[0]).astype(dtype))
+ moving_var = relay.const(np.ones(k_shape[0]).astype(dtype))
+ out, _, _ = relay.nn.batch_norm(
+ out,
+ gamma=gamma,
+ beta=beta,
+ moving_mean=moving_mean,
+ moving_var=moving_var,
+ axis=1,
+ center=True,
+ scale=True,
+ epsilon=1e-5,
+ )
+ sum_data = relay.var("data1", shape=sum_shape, dtype=dtype)
+ out = relay.add(out, sum_data)
+ dic["data1"] = sum_shape
+ param_lst += ["data1"]
+ return relay.nn.relu(out), dic, param_lst
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape,
k_shape, sum_shape=(1, 16, 6, 6), dtype=dtype)
+ conv2d_bn_sum_relu = tvm.IRModule.from_expr(conv2d_bn_sum_relu)
+ config = conv2d_bn_sum_relu, dic, param_lst
+ run_and_verify_func(config, run_module=run_module, dtype=dtype)
+
+ conv2d_bn_sum_relu, dic, param_lst = get_conv2d_bn_sum_relu(x_shape,
k_shape, sum_shape=(1, 16, 1, 1), dtype=dtype)
Review Comment:
Fixed.
##########
tests/python/contrib/test_dnnl.py:
##########
@@ -1732,4 +1771,5 @@ def generate_model(p, c):
if __name__ == "__main__":
- tvm.testing.main()
+ # tvm.testing.main()
+ test_conv2d_bias_sum_relu(True)
Review Comment:
Fixed.
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