Made broadcast unary operator

src/fwd_conv_batchnorm_rewrite.cpp

@@ -59,7 +59,7 @@ void fwd_conv_batchnorm_rewrite::apply(program& p) const
         auto l_weights = p.add_literal({weights.get_shape(), new_weights.data()});
         auto l_bias    = p.add_literal({new_bias.get_shape(), new_bias.data()});
         auto c = p.replace_instruction(conv_ins, conv_op, {conv_ins->inputs()[0], l_weights});
-        auto b = p.insert_instruction(ins, op::broadcast{1}, c, l_bias);
+        auto b = p.insert_instruction(ins, op::broadcast{1, c->get_shape()}, l_bias);
         p.replace_instruction(ins, op::add{}, {c, b});
     }
 }

src/include/migraph/operators.hpp

@@ -618,34 +618,34 @@ struct flatten
 struct broadcast
 {
     uint64_t axis = 0;
+    shape broadcast_shape;
     std::string name() const { return "broadcast"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
         auto t      = inputs.at(0).type();
-        auto result = inputs.at(0);
-        auto input  = inputs.at(1);
+        auto input  = inputs.at(0);
 
-        std::vector<size_t> bcast_strides(result.lens().size(), 0);
+        std::vector<size_t> bcast_strides(broadcast_shape.lens().size(), 0);
 
         if(std::all_of(
-               result.lens().cbegin(), result.lens().cend(), [&](auto x) { return x == 1; }))
+               broadcast_shape.lens().cbegin(), broadcast_shape.lens().cend(), [&](auto x) { return x == 1; }))
         {
             if(axis != 0)
                 MIGRAPH_THROW("when broadcasting tensor of size 1, axis should be 0");
-            return {t, result.lens(), std::move(bcast_strides)};
+            return {t, broadcast_shape.lens(), std::move(bcast_strides)};
         }
         else
         {
-            assert(result.lens().size() - axis >= input.lens().size());
-            if(!std::equal(input.lens().begin(), input.lens().end(), result.lens().begin() + axis))
+            assert(broadcast_shape.lens().size() - axis >= input.lens().size());
+            if(!std::equal(input.lens().begin(), input.lens().end(), broadcast_shape.lens().begin() + axis))
                 MIGRAPH_THROW("when broadcasting success sizes must match");
             std::copy(input.strides().begin(), input.strides().end(), bcast_strides.begin() + axis);
-            return {t, result.lens(), std::move(bcast_strides)};
+            return {t, broadcast_shape.lens(), std::move(bcast_strides)};
         }
     }
     argument compute(context&, shape output_shape, std::vector<argument> args) const
     {
-        return {std::move(output_shape), std::move(args.at(1).data)};
+        return {std::move(output_shape), std::move(args.at(0).data)};
     }
     friend std::ostream& operator<<(std::ostream& os, const broadcast& op)
     {

src/onnx/onnx.cpp

@@ -93,7 +93,7 @@ struct onnx_parser
                     uint64_t axis = (contains(attributes, "axis"))
                                         ? parse_value(attributes.at("axis")).at<uint64_t>()
                                         : 0;
-                    auto l = prog.add_instruction(op::broadcast{axis}, args);
+                    auto l = prog.add_instruction(op::broadcast{axis, args[0]->get_shape()}, args[1]);
                     return prog.add_instruction(x, args[0], l);
                 }
             }
@@ -131,7 +131,7 @@ struct onnx_parser
         {
             uint64_t axis = 1;
             auto l1       = prog.add_instruction(op, args[0], args[1]);
-            auto l2       = prog.add_instruction(op::broadcast{axis}, l1, args[2]);
+            auto l2       = prog.add_instruction(op::broadcast{axis, l1->get_shape()}, args[2]);
             return prog.add_instruction(op::add{}, l1, l2);
         }
         return prog.add_instruction(op, args);
@@ -223,7 +223,7 @@ struct onnx_parser
         {
             uint64_t axis = 1;
             auto l3       = prog.add_instruction(op::gemm{alpha, beta}, l1, l2);
-            auto l4       = prog.add_instruction(op::broadcast{axis}, l3, args[2]);
+            auto l4       = prog.add_instruction(op::broadcast{axis, l3->get_shape()}, args[2]);
             return prog.add_instruction(op::add{}, l3, l4);
         }
         return prog.add_instruction(op::gemm{alpha, beta}, l1, l2);

test/auto_contiguous_test.cpp

 #include <migraph/auto_contiguous.hpp>
 #include <migraph/operators.hpp>
+#include <migraph/instruction.hpp>
 #include <basic_ops.hpp>
 #include <test.hpp>
 
@@ -57,7 +58,7 @@ void after_literal_broadcast()
     auto l2 = p.add_literal(get_2());
     EXPECT(p.get_shape().standard());
     EXPECT(not p.get_shape().broadcasted());
-    auto b = p.add_instruction(migraph::op::broadcast{}, l1, l2);
+    auto b = p.add_instruction(migraph::op::broadcast{0, l1->get_shape()}, l2);
     p.add_instruction(pass_op{}, b);
     EXPECT(not p.get_shape().standard());
     EXPECT(p.get_shape().broadcasted());
@@ -88,7 +89,7 @@ void after_param_broadcast()
     auto l2 = p.add_parameter("2", {migraph::shape::float_type, {2}});
     EXPECT(p.get_shape().standard());
     EXPECT(not p.get_shape().broadcasted());
-    auto b = p.add_instruction(migraph::op::broadcast{}, l1, l2);
+    auto b = p.add_instruction(migraph::op::broadcast{0, l1->get_shape()}, l2);
     p.add_instruction(pass_op{}, b);
     EXPECT(not p.get_shape().standard());
     EXPECT(p.get_shape().broadcasted());

test/cpu_ops_test.cpp

@@ -2,6 +2,7 @@
 #include <vector>
 #include <migraph/literal.hpp>
 #include <migraph/operators.hpp>
+#include <migraph/instruction.hpp>
 #include <migraph/cpu/cpu_target.hpp>
 #include <migraph/verify.hpp>
 #include "test.hpp"
@@ -385,7 +386,7 @@ void broadcast_test()
     uint64_t axis = 0;
     auto l1       = p.add_literal(migraph::literal{a_shape, a_data});
     auto l2       = p.add_literal(migraph::literal{b_shape, b_data});
-    p.add_instruction(migraph::op::broadcast{axis}, l1, l2);
+    p.add_instruction(migraph::op::broadcast{axis, l1->get_shape()}, l2);
     p.compile(migraph::cpu::cpu_target{});
     auto result = p.eval({});
     auto output = result.get<int32_t>();
@@ -404,7 +405,7 @@ void add_broadcast_test()
     uint64_t axis = 0;
     auto l1       = p.add_literal(migraph::literal{a_shape, a_data});
     auto l2       = p.add_literal(migraph::literal{b_shape, b_data});
-    auto l3       = p.add_instruction(migraph::op::broadcast{axis}, l1, l2);
+    auto l3       = p.add_instruction(migraph::op::broadcast{axis, l1->get_shape()}, l2);
     p.add_instruction(migraph::op::add{}, l1, l3);
     p.compile(migraph::cpu::cpu_target{});
     auto result = p.eval({});

test/onnx/onnx_test.cpp

@@ -3,6 +3,7 @@
 #include <migraph/literal.hpp>
 #include <migraph/operators.hpp>
 #include <migraph/program.hpp>
+#include <migraph/instruction.hpp>
 #include <migraph/onnx.hpp>
 #include "test.hpp"
 
@@ -14,7 +15,7 @@ void pytorch_conv_bias_test()
     auto l2       = p.add_parameter("2", {migraph::shape::float_type, {1}});
     uint64_t axis = 1;
     auto l3       = p.add_instruction(migraph::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraph::op::broadcast{axis}, l3, l2);
+    auto l4       = p.add_instruction(migraph::op::broadcast{axis, l3->get_shape()}, l2);
     p.add_instruction(migraph::op::add{}, l3, l4);
 
     auto prog = migraph::parse_onnx("conv.onnx");
@@ -29,7 +30,7 @@ void pytorch_conv_relu_maxpool()
     auto l2       = p.add_parameter("2", {migraph::shape::float_type, {1}});
     uint64_t axis = 1;
     auto l3       = p.add_instruction(migraph::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraph::op::broadcast{axis}, l3, l2);
+    auto l4       = p.add_instruction(migraph::op::broadcast{axis, l3->get_shape()}, l2);
     auto l5       = p.add_instruction(migraph::op::add{}, l3, l4);
     auto l6       = p.add_instruction(migraph::op::activation{"relu"}, l5);
     p.add_instruction(migraph::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l6);
@@ -51,7 +52,7 @@ void pytorch_conv_bn_relu_maxpool()
     auto p6       = p.add_parameter("6", {migraph::shape::float_type, {1}});
     uint64_t axis = 1;
     auto l3       = p.add_instruction(migraph::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraph::op::broadcast{axis}, l3, l2);
+    auto l4       = p.add_instruction(migraph::op::broadcast{axis, l3->get_shape()}, l2);
     auto l5       = p.add_instruction(migraph::op::add{}, l3, l4);
     auto l6       = p.add_instruction(migraph::op::batch_norm_inference{}, l5, p3, p4, p5, p6);
     auto l7       = p.add_instruction(migraph::op::activation{"relu"}, l6);
@@ -69,7 +70,7 @@ void pytorch_conv_relu_maxpool_x2()
     auto l2       = p.add_parameter("2", {migraph::shape::float_type, {5}});
     uint64_t axis = 1;
     auto l3       = p.add_instruction(migraph::op::convolution{}, l0, l1);
-    auto l4       = p.add_instruction(migraph::op::broadcast{axis}, l3, l2);
+    auto l4       = p.add_instruction(migraph::op::broadcast{axis, l3->get_shape()}, l2);
     auto l5       = p.add_instruction(migraph::op::add{}, l3, l4);
     auto l6       = p.add_instruction(migraph::op::activation{"relu"}, l5);
     auto l7 = p.add_instruction(migraph::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l6);
@@ -77,7 +78,7 @@ void pytorch_conv_relu_maxpool_x2()
     auto l8  = p.add_parameter("3", {migraph::shape::float_type, {1, 5, 5, 5}});
     auto l9  = p.add_parameter("4", {migraph::shape::float_type, {1}});
     auto l10 = p.add_instruction(migraph::op::convolution{}, l7, l8);
-    auto l11 = p.add_instruction(migraph::op::broadcast{axis}, l10, l9);
+    auto l11 = p.add_instruction(migraph::op::broadcast{axis, l10->get_shape()}, l9);
     auto l12 = p.add_instruction(migraph::op::add{}, l10, l11);
     auto l13 = p.add_instruction(migraph::op::activation{"relu"}, l12);
     p.add_instruction(migraph::op::pooling{"max", {{0, 0}}, {{2, 2}}, {{2, 2}}}, l13);