Merge branch 'develop' into inner_broadcast_layout

src/onnx/parse_convolution.cpp

@@ -1,7 +1,7 @@
 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2025 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -141,17 +141,14 @@ struct parse_convolution : op_parser<parse_convolution>
         return all_zeros;
     }
 
-    static auto
+    static migraphx::operation
     qparam_broadcast_op(instruction_ref qparam, std::vector<std::size_t> lens, std::size_t axis)
     {
-        if(qparam->get_shape().scalar())
+        if(qparam->get_shape().elements() == 1)
         {
             return migraphx::make_op("multibroadcast", {{"out_lens", lens}});
         }
-        else
-        {
-            return migraphx::make_op("broadcast", {{"out_lens", lens}, {"axis", axis}});
-        }
+        return migraphx::make_op("broadcast", {{"out_lens", lens}, {"axis", axis}});
     }
 
     static instruction_ref handle_quant_bias(const operation& op,
@@ -162,27 +159,37 @@ struct parse_convolution : op_parser<parse_convolution>
                                              const instruction_ref& w_zp,
                                              onnx_parser::node_info& info)
     {
+        // to handle the bias, apply the following transformation:
+        // conv(x-x_zp,w-w_zp) = conv(x,w) - conv(x_zp,w) - conv(x,w_zp) + conv(x_zp,w_zp)
         instruction_ref ret = input;
+
+        // multibroadcast (or broadcast) zero points according to spec
+        // x_zp should be a scalar or literal with one element
+        // w_zp can be either a single element or a 1d tensor with size out_channels
+        migraphx::operation x_zp_bc =
+            migraphx::make_op("multibroadcast", {{"out_lens", x->get_shape().lens()}});
+        migraphx::operation w_zp_bc = qparam_broadcast_op(w_zp, weights->get_shape().lens(), 0);
+
         if(not is_symmetric_zero_point(x_zp))
         {
-            auto out_zp_1 = info.add_common_op(op.name(), x_zp, weights);
+            auto x_zp_mb  = info.add_instruction(x_zp_bc, x_zp);
+            auto out_zp_1 = info.add_instruction(op, x_zp_mb, weights);
             ret           = info.add_common_op("sub", ret, out_zp_1);
         }
 
         if(not is_symmetric_zero_point(w_zp))
         {
-            auto out_zp_2 = info.add_common_op(op.name(), x, w_zp);
+            auto w_zp_mb  = info.add_instruction(w_zp_bc, w_zp);
+            auto out_zp_2 = info.add_instruction(op, x, w_zp_mb);
             ret           = info.add_common_op("sub", ret, out_zp_2);
         }
 
         if(not(is_symmetric_zero_point(x_zp)) and not(is_symmetric_zero_point(w_zp)))
         {
-            auto x_zp_bc =
-                info.add_instruction(qparam_broadcast_op(x_zp, x->get_shape().lens(), 0), x_zp);
-            auto w_zp_bc = info.add_instruction(
-                qparam_broadcast_op(w_zp, weights->get_shape().lens(), 0), w_zp);
+            auto x_zp_mb = info.add_instruction(x_zp_bc, x_zp);
+            auto w_zp_mb = info.add_instruction(w_zp_bc, w_zp);
 
-            auto out_zp_3 = info.add_instruction(op, x_zp_bc, w_zp_bc);
+            auto out_zp_3 = info.add_instruction(op, x_zp_mb, w_zp_mb);
 
             ret = info.add_common_op("add", ret, out_zp_3);
         }

src/simplify_algebra.cpp

@@ -1,7 +1,7 @@
 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2025 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -761,6 +761,8 @@ struct find_inner_broadcast
     void apply(module& m, const match::matcher_result& r) const
     {
         auto ins               = r.result;
+        if(ins->get_operator().name() == "layout")
+            return;
         const auto& broadcasts = ins->inputs();
         if(broadcasts.empty())
             return;

test/onnx/.onnxrt-commit

@@ -1 +1 @@
-1ce59577d54be75afdb6123cd465f18456d78611
+a05203b91c96ca5ca0112f9403267c04da55ec78

test/onnx/convinteger_bias_test.onnx

@@ -7,13 +7,13 @@
 strides@
@
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convinteger_bias_testZ
 
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@@ -23,7 +23,7 @@
 
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+
 
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test/onnx/convinteger_dual_bias_simple_test.onnx

@@ -0,0 +1,34 @@
+	!convinteger_dual_bias_simple_test:�

+B
+
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+
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+
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+
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+	dilations@
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+strides@
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!convinteger_dual_bias_simple_testZ
+
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+
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+
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+
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+

+
+B

test/onnx/convinteger_dual_bias_test.onnx

@@ -8,16 +8,18 @@ B
 strides@
@
�
convinteger_dual_bias_testZ
 
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+
 
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-Z
+
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@@ -28,7 +30,7 @@ B
 
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+
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test/onnx/gen_onnx.py

@@ -1670,10 +1670,10 @@ def convinteger_no_bias_uint8_test():
 
 @onnx_test()
 def convinteger_bias_test():
-    x = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 3, 32, 32])
-    y = helper.make_tensor_value_info('1', TensorProto.INT8, [1, 3, 5, 5])
+    x = helper.make_tensor_value_info('0', TensorProto.INT8, [2, 3, 32, 32])
+    y = helper.make_tensor_value_info('1', TensorProto.INT8, [4, 3, 5, 5])
     z = helper.make_tensor_value_info('2', TensorProto.INT8, [1])
-    out = helper.make_tensor_value_info('3', TensorProto.INT32, [1, 2, 28, 28])
+    out = helper.make_tensor_value_info('3', TensorProto.INT32, [2, 4, 28, 28])
 
     node = onnx.helper.make_node('ConvInteger',
                                  inputs=['0', '1', '2'],
@@ -1686,6 +1686,23 @@ def convinteger_bias_test():
 
 @onnx_test()
 def convinteger_dual_bias_test():
+    x = helper.make_tensor_value_info('0', TensorProto.INT8, [2, 3, 10, 10])
+    y = helper.make_tensor_value_info('1', TensorProto.INT8, [4, 3, 3, 3])
+    z = helper.make_tensor_value_info('2', TensorProto.INT8, [1])
+    w = helper.make_tensor_value_info('3', TensorProto.INT8, [1])
+    out = helper.make_tensor_value_info('4', TensorProto.INT32, [2, 4, 8, 8])
+
+    node = onnx.helper.make_node('ConvInteger',
+                                 inputs=['0', '1', '2', '3'],
+                                 outputs=['4'],
+                                 dilations=[1, 1],
+                                 strides=[1, 1])
+
+    return ([node], [x, y, z, w], [out])
+
+
+@onnx_test()
+def convinteger_dual_bias_simple_test():
     x = helper.make_tensor_value_info('0', TensorProto.INT8, [1, 3, 5, 5])
     y = helper.make_tensor_value_info('1', TensorProto.INT8, [1, 3, 2, 2])
     z = helper.make_tensor_value_info('2', TensorProto.INT8, [1])

test/onnx/parse/convinteger_bias_test.cpp

@@ -1,7 +1,7 @@
 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2025 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -28,24 +28,20 @@ TEST_CASE(convinteger_bias_test)
 {
     migraphx::program p;
     auto* mm       = p.get_main_module();
-    auto data      = mm->add_parameter("0", {migraphx::shape::int8_type, {1, 3, 32, 32}});
-    auto weights   = mm->add_parameter("1", {migraphx::shape::int8_type, {1, 3, 5, 5}});
+    auto data      = mm->add_parameter("0", {migraphx::shape::int8_type, {2, 3, 32, 32}});
+    auto weights   = mm->add_parameter("1", {migraphx::shape::int8_type, {4, 3, 5, 5}});
     auto data_bias = mm->add_parameter("2", {migraphx::shape::int8_type, {1}, {1}});
 
     mm->add_literal(migraphx::literal{migraphx::shape{data->get_shape().type(), {1}, {0}}, {0}});
     auto quant = mm->add_instruction(migraphx::make_op("quant_convolution"), data, weights);
 
     auto bcast_data_bias = mm->add_instruction(
-        migraphx::make_op("multibroadcast", {{"out_lens", weights->get_shape().lens()}}),
-        data_bias);
+        migraphx::make_op("multibroadcast", {{"out_lens", data->get_shape().lens()}}), data_bias);
 
     auto quant2 =
         mm->add_instruction(migraphx::make_op("quant_convolution"), bcast_data_bias, weights);
 
-    auto bcast_quant2 = mm->add_instruction(
-        migraphx::make_op("multibroadcast", {{"out_lens", quant->get_shape().lens()}}), quant2);
-
-    mm->add_instruction(migraphx::make_op("sub"), quant, bcast_quant2);
+    mm->add_instruction(migraphx::make_op("sub"), quant, quant2);
 
     auto prog = optimize_onnx("convinteger_bias_test.onnx");
     EXPECT(p == prog);

test/onnx/parse/convinteger_dual_bias_test.cpp

@@ -1,7 +1,7 @@
 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2024 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2025 Advanced Micro Devices, Inc. All rights reserved.
  *
  * Permission is hereby granted, free of charge, to any person obtaining a copy
  * of this software and associated documentation files (the "Software"), to deal
@@ -28,41 +28,37 @@ TEST_CASE(convinteger_dual_bias_test)
 {
     migraphx::program p;
     auto* mm         = p.get_main_module();
-    auto data        = mm->add_parameter("0", {migraphx::shape::int8_type, {1, 3, 5, 5}});
-    auto weight      = mm->add_parameter("1", {migraphx::shape::int8_type, {1, 3, 2, 2}});
+    auto data        = mm->add_parameter("0", {migraphx::shape::int8_type, {2, 3, 10, 10}});
+    auto weight      = mm->add_parameter("1", {migraphx::shape::int8_type, {4, 3, 3, 3}});
     auto data_bias   = mm->add_parameter("2", {migraphx::shape::int8_type, {1}, {1}});
     auto weight_bias = mm->add_parameter("3", {migraphx::shape::int8_type, {1}, {1}});
 
     auto quant = mm->add_instruction(migraphx::make_op("quant_convolution"), data, weight);
 
     auto mbcast_data_bias = mm->add_instruction(
-        migraphx::make_op("multibroadcast", {{"out_lens", weight->get_shape().lens()}}), data_bias);
+        migraphx::make_op("multibroadcast", {{"out_lens", data->get_shape().lens()}}), data_bias);
 
-    auto quant_db_w =
+    auto quant_mb_w =
         mm->add_instruction(migraphx::make_op("quant_convolution"), mbcast_data_bias, weight);
 
-    auto quant_mb_w = mm->add_instruction(
-        migraphx::make_op("multibroadcast", {{"out_lens", quant->get_shape().lens()}}), quant_db_w);
-
     quant = mm->add_instruction(migraphx::make_op("sub"), quant, quant_mb_w);
 
     auto mbcast_weight_bias = mm->add_instruction(
-        migraphx::make_op("multibroadcast", {{"out_lens", data->get_shape().lens()}}), weight_bias);
+        migraphx::make_op("multibroadcast", {{"out_lens", weight->get_shape().lens()}}),
+        weight_bias);
 
-    auto quant_d_wb =
+    auto quant_md_wb =
         mm->add_instruction(migraphx::make_op("quant_convolution"), data, mbcast_weight_bias);
 
-    auto quant_md_wb = mm->add_instruction(
-        migraphx::make_op("multibroadcast", {{"out_lens", quant->get_shape().lens()}}), quant_d_wb);
-
     quant = mm->add_instruction(migraphx::make_op("sub"), quant, quant_md_wb);
 
-    auto bcast_data_bias = mm->add_instruction(
-        migraphx::make_op("broadcast", {{"out_lens", data->get_shape().lens()}}), data_bias);
-    auto bcast_weight_bias = mm->add_instruction(
-        migraphx::make_op("broadcast", {{"out_lens", weight->get_shape().lens()}}), weight_bias);
+    mbcast_data_bias = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", data->get_shape().lens()}}), data_bias);
+    mbcast_weight_bias = mm->add_instruction(
+        migraphx::make_op("multibroadcast", {{"out_lens", weight->get_shape().lens()}}),
+        weight_bias);
     auto bias_quant = mm->add_instruction(
-        migraphx::make_op("quant_convolution"), bcast_data_bias, bcast_weight_bias);
+        migraphx::make_op("quant_convolution"), mbcast_data_bias, mbcast_weight_bias);
 
     mm->add_instruction(migraphx::make_op("add"), quant, bias_quant);