Merge branch 'develop' of https://github.com/ROCmSoftwarePlatform/AMDMIGraphX into int8_quantize

src/targets/gpu/CMakeLists.txt

@@ -24,9 +24,11 @@ add_library(migraphx_device
     device/tan.cpp
     device/sinh.cpp
     device/cosh.cpp
+    device/tanh.cpp
     device/asin.cpp
     device/acos.cpp
     device/atan.cpp
+    device/relu.cpp
     device/add_relu.cpp
     device/contiguous.cpp
     device/logsoftmax.cpp
@@ -72,9 +74,7 @@ add_library(migraphx_gpu
     logsoftmax.cpp
     contiguous.cpp
     concat.cpp
-    relu.cpp
     leaky_relu.cpp
-    tanh.cpp
     batchnorm.cpp
     write_literals.cpp
     rocblas.cpp

src/targets/gpu/device/relu.cpp

+#include <migraphx/gpu/device/relu.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void relu(hipStream_t stream, const argument& result, const argument& arg)
+{
+    nary(stream, result, arg)([](auto x) { return std::max<decltype(x)>(0, x); });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/device/tanh.cpp

+#include <migraphx/gpu/device/tanh.hpp>
+#include <migraphx/gpu/device/nary.hpp>
+#include <migraphx/gpu/device/types.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void tanh(hipStream_t stream, const argument& result, const argument& arg)
+{
+    nary(stream, result, arg)([](auto x) { return ::tanh(to_hip_type(x)); });
+}
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx

src/targets/gpu/include/migraphx/gpu/device/relu.hpp

+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_RELU_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_RELU_HPP
+
+#include <migraphx/argument.hpp>
+#include <migraphx/config.hpp>
+#include <hip/hip_runtime_api.h>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void relu(hipStream_t stream, const argument& result, const argument& arg);
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/targets/gpu/include/migraphx/gpu/device/tanh.hpp

+#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_TANH_HPP
+#define MIGRAPHX_GUARD_RTGLIB_DEVICE_TANH_HPP
+
+#include <migraphx/argument.hpp>
+#include <migraphx/config.hpp>
+#include <hip/hip_runtime_api.h>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace gpu {
+namespace device {
+
+void tanh(hipStream_t stream, const argument& result, const argument& arg);
+
+} // namespace device
+} // namespace gpu
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx
+
+#endif

src/targets/gpu/include/migraphx/gpu/relu.hpp

 #ifndef MIGRAPHX_GUARD_RTGLIB_RELU_HPP
 #define MIGRAPHX_GUARD_RTGLIB_RELU_HPP
 
-#include <migraphx/shape.hpp>
-#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/oper.hpp>
+#include <migraphx/gpu/device/relu.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
@@ -10,24 +10,8 @@ namespace gpu {
 
 struct context;
 
-struct miopen_relu
+struct hip_relu : unary_device<hip_relu, device::relu>
 {
-    shared<activation_descriptor> ad;
-
-    template <class Self, class F>
-    static auto reflect(Self& self, F f)
-    {
-        return gpu::reflect(self.ad.get(), f);
-    }
-
-    std::string name() const { return "gpu::relu"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
-    argument
-    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
-    {
-        return shapes.size() - 1;
-    }
 };
 
 } // namespace gpu

src/targets/gpu/include/migraphx/gpu/tanh.hpp

 #ifndef MIGRAPHX_GUARD_RTGLIB_TANH_HPP
 #define MIGRAPHX_GUARD_RTGLIB_TANH_HPP
 
-#include <migraphx/shape.hpp>
-#include <migraphx/gpu/miopen.hpp>
+#include <migraphx/gpu/oper.hpp>
+#include <migraphx/gpu/device/tanh.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace gpu {
 
-struct context;
-
-struct miopen_tanh
+struct hip_tanh : unary_device<hip_tanh, device::tanh>
 {
-    shared<activation_descriptor> ad;
-
-    template <class Self, class F>
-    static auto reflect(Self& self, F f)
-    {
-        return gpu::reflect(self.ad.get(), f);
-    }
-
-    std::string name() const { return "gpu::tanh"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
-    argument
-    compute(context& ctx, const shape& output_shape, const std::vector<argument>& args) const;
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
-    {
-        return shapes.size() - 1;
-    }
 };
 
 } // namespace gpu

src/targets/gpu/lowering.cpp

@@ -87,10 +87,8 @@ struct miopen_apply
     void init()
     {
         this->last = instruction::get_output_alias(std::prev(prog->end()));
-        add_miopen_simple_op<miopen_relu>("relu", make_relu);
         add_miopen_simple_op<miopen_sigmoid>("sigmoid", make_sigmoid);
         add_miopen_simple_op<miopen_abs>("abs", make_abs);
-        add_miopen_simple_op<miopen_tanh>("tanh", make_tanh);
 
         add_miopen_extend_op<miopen_leaky_relu, op::leaky_relu>("leaky_relu", make_leaky_relu);
         add_miopen_extend_op<miopen_elu, op::elu>("elu", make_elu);
@@ -105,6 +103,7 @@ struct miopen_apply
         add_generic_op<hip_tan>("tan");
         add_generic_op<hip_sinh>("sinh");
         add_generic_op<hip_cosh>("cosh");
+        add_generic_op<hip_tanh>("tanh");
         add_generic_op<hip_asin>("asin");
         add_generic_op<hip_acos>("acos");
         add_generic_op<hip_atan>("atan");
@@ -117,6 +116,7 @@ struct miopen_apply
         add_generic_op<hip_round>("round");
         add_generic_op<hip_pow>("pow");
         add_generic_op<hip_sqdiff>("sqdiff");
+        add_generic_op<hip_relu>("relu");
         add_generic_op<hip_sign>("sign");
 
         add_extend_op<miopen_gemm, op::dot>("dot");

src/targets/gpu/relu.cpp

-#include <migraphx/gpu/relu.hpp>
-#include <migraphx/gpu/context.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape miopen_relu::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{inputs, *this}.has(2).not_broadcasted();
-    return inputs.at(1);
-}
-
-argument miopen_relu::compute(context& ctx,
-                              const shape& output_shape,
-                              const std::vector<argument>& args) const
-{
-    float alpha = 1;
-    float beta  = 0;
-    auto x_desc = make_tensor(args[0].get_shape());
-    auto y_desc = make_tensor(output_shape);
-    miopenActivationForward(ctx.get_stream().get_miopen(),
-                            ad.get(),
-                            &alpha,
-                            x_desc.get(),
-                            args[0].implicit(),
-                            &beta,
-                            y_desc.get(),
-                            args[1].implicit());
-
-    return args[1];
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/targets/gpu/tanh.cpp

-#include <migraphx/gpu/tanh.hpp>
-#include <migraphx/gpu/context.hpp>
-
-namespace migraphx {
-inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape miopen_tanh::compute_shape(const std::vector<shape>& inputs) const
-{
-    check_shapes{inputs, *this}.has(2).packed();
-    return inputs.at(0);
-}
-
-argument miopen_tanh::compute(context& ctx,
-                              const shape& output_shape,
-                              const std::vector<argument>& args) const
-{
-    float alpha = 1;
-    float beta  = 0;
-    auto x_desc = make_tensor(args[0].get_shape());
-    auto y_desc = make_tensor(output_shape);
-    miopenActivationForward(ctx.get_stream().get_miopen(),
-                            ad.get(),
-                            &alpha,
-                            x_desc.get(),
-                            args[0].implicit(),
-                            &beta,
-                            y_desc.get(),
-                            args[1].implicit());
-
-    return args[1];
-}
-
-} // namespace gpu
-} // namespace MIGRAPHX_INLINE_NS
-} // namespace migraphx

src/tf/tf.cpp

@@ -181,6 +181,7 @@ struct tf_parser
         add_mem_op("Pack", &tf_parser::parse_pack, false);
         add_mem_op("Pad", &tf_parser::parse_pad);
         add_mem_op("Reshape", &tf_parser::parse_reshape, false);
+        add_mem_op("Slice", &tf_parser::parse_slice, false);
         add_mem_op("Softmax", &tf_parser::parse_softmax<op::softmax>);
         add_mem_op("Squeeze", &tf_parser::parse_squeeze, false);
         add_mem_op("StridedSlice", &tf_parser::parse_stridedslice);
@@ -733,6 +734,29 @@ struct tf_parser
         }
     }
 
+    instruction_ref
+    parse_slice(const std::string&, const attribute_map&, std::vector<instruction_ref> args)
+    {
+        op::slice op;
+        auto starts     = args[1]->eval().get<int32_t>().to_vector();
+        auto size       = args[2]->eval().get<int32_t>().to_vector();
+        auto axes       = args[0]->get_shape().lens();
+        size_t num_axes = axes.size();
+
+        op.starts = std::vector<int64_t>(starts.begin(), starts.end());
+        op.ends   = std::vector<int64_t>(num_axes);
+        op.axes   = std::vector<int64_t>(num_axes);
+        std::iota(op.axes.begin(), op.axes.end(), 0);
+        for(size_t i = 0; i < num_axes; i++)
+        {
+            if(size[i] == -1)
+                op.ends[i] = axes[i];
+            else
+                op.ends[i] = starts[i] + size[i];
+        }
+        return prog.add_instruction(op, make_contiguous(args[0]));
+    }
+
     // template to facilitate the logsoftmax later
     template <class Op>
     instruction_ref parse_softmax(const std::string&,

test/tf/tf_test.cpp

@@ -412,6 +412,26 @@ TEST_CASE(rsqrt_test)
     EXPECT(p == prog);
 }
 
+TEST_CASE(slice_test)
+{
+    migraphx::program p;
+    std::size_t num_axes = 2;
+    auto l0 = p.add_parameter("0", migraphx::shape{migraphx::shape::float_type, {5, 10}});
+    migraphx::shape s0{migraphx::shape::int32_type, {num_axes}};
+    p.add_literal(migraphx::literal{s0, {1, 0}});
+    p.add_literal(migraphx::literal{s0, {2, -1}});
+
+    migraphx::op::slice op;
+    op.starts = {1, 0};
+    op.ends   = {3, 10};
+    op.axes   = std::vector<int64_t>(num_axes);
+    std::iota(op.axes.begin(), op.axes.end(), 0);
+    p.add_instruction(op, l0);
+    auto prog = optimize_tf("slice_test.pb", false);
+
+    EXPECT(p == prog);
+}
+
 TEST_CASE(softmax_test)
 {
     migraphx::program p;