Fix rank 2 batch norm (#1412)

Allows for rank 2 tensors into batchnorm.  Specifically when spatial dimensions are all 1 and removed

src/onnx/parse_batchnorm.cpp

@@ -54,18 +54,19 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
             MIGRAPHX_THROW("PARSE_BATCHNORM: argument scale, bias, mean, or var rank != 1");
         }
 
-        if(x_lens.size() == 1)
+        auto x_rank = x_lens.size();
+        if(x_rank == 1 or x_rank == 2)
         {
             auto rt      = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {0.5}});
             auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_type}, {epsilon}});
-            auto n0   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
-            auto d0   = info.add_broadcastable_binary_op("add", args[4], eps);
-            auto d1   = info.add_broadcastable_binary_op("pow", d0, rt);
-            auto div0 = info.add_broadcastable_binary_op("div", n0, d1);
+            auto numer   = info.add_broadcastable_binary_op("sub", args[0], args[3]);
+            auto var_eps = info.add_broadcastable_binary_op("add", args[4], eps);
+            auto denom   = info.add_broadcastable_binary_op("pow", var_eps, rt);
+            auto div0    = info.add_broadcastable_binary_op("div", numer, denom);
             auto r0      = info.add_broadcastable_binary_op("mul", div0, args[1]);
             return info.add_broadcastable_binary_op("add", r0, args[2]);
         }
-        else if(x_lens.size() > 2)
+        else if(x_rank > 2)
         {
             // unsqueeze tensors of shape (C) to broadcast correctly
             std::vector<int64_t> unsqueeze_axes(x_lens.size() - 2);
@@ -89,7 +90,7 @@ struct parse_batchnorm : op_parser<parse_batchnorm>
         }
         else
         {
-            // num dims either 0 or 2
+            // rank == 0
             MIGRAPHX_THROW("PARSE_BATCHNORM: rank " + std::to_string(x_lens.size()) +
                            " input tensor, unhandled data format");
         }

test/onnx/batch_norm_invalid_rank_test.onnx → test/onnx/batch_norm_rank_2_test.onnx

-batch_norm_invalid_rank_test:

-7
+batch_norm_rank_2_test:

+J
 
x
 scale
 bias
 mean
-variance
y"BatchNormalizationbatch_norm_invalid_rank_testZ
+variance
y"BatchNormalization*
+epsilon75

batch_norm_rank_2_testZ
 
x
 

-
-Z
+
+Z
 scale
 
 

-Z
+Z
 bias
 
 

-Z
+Z
 mean
 
 

-Z
+Z
 variance
 
 

-b
+b
 
y
 

-
-B
\ No newline at end of file
+
+B
\ No newline at end of file

test/onnx/gen_onnx.py

@@ -331,6 +331,24 @@ def batch_norm_flat_test():
     return ([node], [x, scale, bias, mean, var], [out])
 
 
+@onnx_test
+def batch_norm_rank_2_test():
+    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 5])
+    scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [5])
+    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [5])
+    mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [5])
+    var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [5])
+    out = helper.make_tensor_value_info('y', TensorProto.FLOAT, [2, 5])
+
+    node = onnx.helper.make_node(
+        'BatchNormalization',
+        inputs=['x', 'scale', 'bias', 'mean', 'variance'],
+        outputs=['y'],
+        epsilon=1e-6)
+
+    return ([node], [x, scale, bias, mean, var], [out])
+
+
 @onnx_test
 def batch_norm_1d_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT16, [2, 3, 4])
@@ -385,23 +403,6 @@ def batch_norm_3d_test():
     return ([node], [x, scale, bias, mean, var], [out])
 
 
-@onnx_test
-def batch_norm_invalid_rank_test():
-    x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [8, 8])
-    scale = helper.make_tensor_value_info('scale', TensorProto.FLOAT, [8])
-    bias = helper.make_tensor_value_info('bias', TensorProto.FLOAT, [8])
-    mean = helper.make_tensor_value_info('mean', TensorProto.FLOAT, [8])
-    var = helper.make_tensor_value_info('variance', TensorProto.FLOAT, [8])
-    out = helper.make_tensor_value_info('y', TensorProto.FLOAT, [8, 8])
-
-    node = onnx.helper.make_node(
-        'BatchNormalization',
-        inputs=['x', 'scale', 'bias', 'mean', 'variance'],
-        outputs=['y'])
-
-    return ([node], [x, scale, bias, mean, var], [out])
-
-
 @onnx_test
 def batch_norm_invalid_bias_rank_test():
     x = helper.make_tensor_value_info('x', TensorProto.FLOAT, [2, 3, 4, 4])

test/onnx/onnx_test.cpp

@@ -394,6 +394,31 @@ TEST_CASE(batch_norm_flat_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(batch_norm_rank_2_test)
+{
+    migraphx::program p;
+    auto* mm = p.get_main_module();
+
+    auto x     = mm->add_parameter("x", {migraphx::shape::float_type, {2, 5}});
+    auto scale = mm->add_parameter("scale", {migraphx::shape::float_type, {5}});
+    auto bias  = mm->add_parameter("bias", {migraphx::shape::float_type, {5}});
+    auto mean  = mm->add_parameter("mean", {migraphx::shape::float_type, {5}});
+    auto var   = mm->add_parameter("variance", {migraphx::shape::float_type, {5}});
+
+    auto rt  = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {0.5}});
+    auto eps = mm->add_literal(migraphx::literal{migraphx::shape::float_type, {1e-6f}});
+
+    auto numer   = add_common_op(*mm, migraphx::make_op("sub"), {x, mean});
+    auto var_eps = add_common_op(*mm, migraphx::make_op("add"), {var, eps});
+    auto denom   = add_common_op(*mm, migraphx::make_op("pow"), {var_eps, rt});
+    auto div0    = add_common_op(*mm, migraphx::make_op("div"), {numer, denom});
+    auto r0      = add_common_op(*mm, migraphx::make_op("mul"), {div0, scale});
+    add_common_op(*mm, migraphx::make_op("add"), {r0, bias});
+
+    auto prog = optimize_onnx("batch_norm_rank_2_test.onnx");
+    EXPECT(p == prog);
+}
+
 TEST_CASE(batch_norm_1d_test)
 {
     migraphx::program p;

test/onnx/verify_onnx.cpp

@@ -115,6 +115,43 @@ TEST_CASE(batch_norm_flat_test)
     EXPECT(migraphx::verify_range(result_vector, gold));
 }
 
+TEST_CASE(batch_norm_rank_2_test)
+{
+    migraphx::program p = migraphx::parse_onnx("batch_norm_rank_2_test.onnx");
+    p.compile(migraphx::ref::target{});
+
+    migraphx::shape x_shape{migraphx::shape::float_type, {2, 5}};
+    migraphx::shape c_shape(migraphx::shape::float_type, {5});
+    std::vector<float> x_data = {1., 2., 3., 4., 5., 6., 7., 8., 9., 10.};
+    std::vector<float> scale_data(5, 1.);
+    std::vector<float> bias_data(5, 0.);
+    std::vector<float> mean_data = {1., 2., 1., 2., 1.};
+    std::vector<float> variance_data(5, 0.5);
+
+    migraphx::parameter_map params;
+    params["x"]        = migraphx::argument(x_shape, x_data.data());
+    params["scale"]    = migraphx::argument(c_shape, scale_data.data());
+    params["bias"]     = migraphx::argument(c_shape, bias_data.data());
+    params["mean"]     = migraphx::argument(c_shape, mean_data.data());
+    params["variance"] = migraphx::argument(c_shape, variance_data.data());
+
+    auto result = p.eval(params).back();
+    std::vector<float> result_vector;
+    result.visit([&](auto output) { result_vector.assign(output.begin(), output.end()); });
+
+    std::vector<float> gold = {0.,
+                               0.,
+                               2.8284243,
+                               2.8284243,
+                               5.65684859,
+                               7.07106074,
+                               7.07106074,
+                               9.89948504,
+                               9.89948504,
+                               12.72790933};
+    EXPECT(migraphx::verify_range(result_vector, gold));
+}
+
 TEST_CASE(batch_norm_1d_test)
 {
     migraphx::program p = migraphx::parse_onnx("batch_norm_1d_test.onnx");