Merge branch 'develop' into optimize_jenkinsfile

src/targets/gpu/include/migraphx/gpu/int8_conv_pack.hpp → src/include/migraphx/op/isinf.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -21,31 +21,32 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_INT8_CONV_PACK_HPP
-#define MIGRAPHX_GUARD_RTGLIB_INT8_CONV_PACK_HPP
+#ifndef MIGRAPHX_GUARD_OPERATORS_ISINF_HPP
+#define MIGRAPHX_GUARD_OPERATORS_ISINF_HPP
 
-#include <migraphx/argument.hpp>
+#include <migraphx/op/unary.hpp>
 #include <migraphx/config.hpp>
-#include <utility>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
+namespace op {
 
-struct context;
-
-struct miopen_int8_conv_pack
+struct isinf : unary<isinf>
 {
-    std::string name() const { return "gpu::int8_conv_pack"; }
-    shape compute_shape(const std::vector<shape>& inputs) const;
-    argument compute(context& ctx, const shape&, const std::vector<argument>& args) const;
-    std::ptrdiff_t output_alias(const std::vector<shape>& shapes) const
+    auto apply() const
+    {
+        return [&](auto x) { return std::isinf(static_cast<double>(x)); };
+    }
+
+    std::string name() const { return "isinf"; }
+
+    shape compute_shape(std::vector<shape> inputs) const
     {
-        return shapes.size() - 1;
+        return unary<isinf>::compute_shape(std::move(inputs)).with_type(shape::bool_type);
     }
 };
 
-} // namespace gpu
+} // namespace op
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/op/multinomial.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -21,11 +21,52 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
+
+/**
+ *  * Multinomial or categorical distribution.  Performs a sampling of random input
+ *         and returns a count of
+ *         each category, or bucket.  This does not require the standard multinomial
+ *         distribution but instead takes a probability distribution, i.e. cumulative
+ *         distribution function (CDF) as its first input.
+ *
+ *      Inputs:   args[0] - a tensor of probabilities for each category.  Values are
+ *                          cumulative density function
+ *                          totals as provided by operation prefix_scan_sum.  Values are
+ *                          cumulative probabilities (i.e. start with any set of numbers > 0
+ *                          and then apply prefix_scan_sum).  Values do not need to be
+ *                          normalized to sum to 1; this is done in runtime computation.
+ *
+ *                          This input has Rank 2.  Dimension 0 is batch #, so that there can be
+ *                          a different CDF for each iteration in the batch.  The size of dimension
+ *                          1 is the number of categories.
+ *
+ *                args[1] - a tensor of random numbers.  The last dimension is the sample
+ *                          size, i.e. the number of
+ *                          random samples in each iteration of the batch.  Nominally
+ *                          has two dimensions where the first dimension is batch size, but
+ *                          any reshaping such that the total
+ *                          number of elements is (batch_size * sample_size) is legal.
+ *
+ *                          Values as created by a std::mt19937 like this:
+ *
+ *                           size_t sample_size = 100000;
+ *                           float seed         = 0.0f;
+ *                           std::mt19937 gen(seed);
+ *                           std::uniform_real_distribution<> dis(0.0, 1.0);
+ *                           std::vector<float> rand_samples(sample_size);
+ *                           std::generate(rand_samples.begin(), rand_samples.end(), [&]() { return
+ *                                dis(gen); });
+ *
+ *        Output:   A 2D vector of category each input.  Dimensions are (Input 1[first], Input
+ 2[last]).
+ *
+*/
 #ifndef MIGRAPHX_GUARD_OPERATORS_MULTINOMIAL_HPP
 #define MIGRAPHX_GUARD_OPERATORS_MULTINOMIAL_HPP
 
-#include <migraphx/check_shapes.hpp>
 #include <migraphx/argument.hpp>
+#include <migraphx/check_shapes.hpp>
+#include <migraphx/dyn_output.hpp>
 #include <migraphx/par_for.hpp>
 #include <migraphx/reflect.hpp>
 #include <random>
@@ -47,22 +88,35 @@ struct multinomial
     std::string name() const { return "multinomial"; }
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this}.has(2).only_dims(2);
-        size_t sample_size = inputs.back().lens().back();
+        check_shapes{inputs, *this, true}.has(2).only_dims(2);
 
-        if(not contains({shape::int32_type, shape::int64_type}, dtype))
-            MIGRAPHX_THROW(
-                "Multinomial: Invalid output type. Valid types are int32_type and int64_type.");
+        if(inputs.back().ndim() < 1)
+            MIGRAPHX_THROW("Multinomial: Second input shape (sample) has no dimensions");
+        if(dtype == shape::bool_type)
+            MIGRAPHX_THROW("Multinomial: boolean output type invalid.");
 
-        return {dtype, {inputs.front().lens().front(), sample_size}};
+        // Output takes one dimension from each of the two input shapes.  If they are both fixed,
+        // return a static shape
+        if((not inputs.front().dynamic()) or (inputs.front().dyn_dims().front().is_fixed()))
+        {
+            if((not inputs.back().dynamic()) or (inputs.back().dyn_dims().back().is_fixed()))
+            {
+                size_t batch = {inputs.front().max_lens().front()};
+                size_t sample_size{inputs.back().max_lens().back()};
+                return {dtype, {batch, sample_size}};
+            }
+        }
+        return {dtype,
+                {inputs.front().to_dynamic().dyn_dims().front(),
+                 inputs.back().to_dynamic().dyn_dims().back()}};
     }
 
-    argument compute(const shape& output_shape, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
-        argument result{output_shape};
-        size_t batch_size  = output_shape.lens().front();
+        argument result{dyn_out.computed_shape};
+        size_t batch_size  = dyn_out.computed_shape.lens().front();
         size_t class_size  = args[0].get_shape().lens().back();
-        size_t sample_size = output_shape.lens().back();
+        size_t sample_size = dyn_out.computed_shape.lens().back();
 
         visit_all(args[0], args[1])([&](auto cdf, auto dist) {
             result.visit([&](auto output) {
@@ -70,13 +124,16 @@ struct multinomial
                     auto idx       = args[1].get_shape().multi(i);
                     auto cdf_begin = cdf.begin() + (idx[0] * class_size);
                     auto cdf_end   = cdf_begin + class_size;
+
+                    // std::upper_bound returns an iterator to the bucket the value belongs in,
+                    // when normalized by the probability distribution dist
                     auto sample_iter =
                         std::upper_bound(cdf_begin, cdf_end, dist[i] * *(std::prev(cdf_end)));
+                    // convert iterator to an integer index
                     output[i] = std::distance(cdf_begin, sample_iter);
                 });
             });
         });
-
         return result;
     }
 };

src/targets/gpu/gather.cpp → src/include/migraphx/op/nearbyint.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -21,25 +21,30 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#include <migraphx/gpu/gather.hpp>
-#include <migraphx/gpu/context.hpp>
-#include <migraphx/gpu/device/gather.hpp>
+#ifndef MIGRAPHX_GUARD_OPERATORS_NEARBYINT_HPP
+#define MIGRAPHX_GUARD_OPERATORS_NEARBYINT_HPP
+
+#include <migraphx/op/unary.hpp>
+#include <migraphx/config.hpp>
+#include <fenv.h>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-
-shape hip_gather::compute_shape(std::vector<shape> inputs) const
-{
-    inputs.pop_back();
-    return op.normalize_compute_shape(inputs);
-}
-
-argument hip_gather::compute(context& ctx, const shape&, const std::vector<argument>& args) const
+namespace op {
+struct nearbyint : unary<nearbyint>
 {
-    return device::gather(ctx.get_stream().get(), args.back(), args[0], args[1], op.axis);
-}
-
-} // namespace gpu
+    auto apply() const
+    {
+        return [](auto x) {
+            auto rounding_mode = fegetround();
+            fesetround(FE_TONEAREST);
+            return std::nearbyint(x);
+            fesetround(rounding_mode);
+        };
+    }
+};
+} // namespace op
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
+
+#endif

src/include/migraphx/op/normalize_attribute.hpp

@@ -40,6 +40,8 @@ namespace op {
  * 2. use_rank (default) vs use_len:
  *  `use_rank` sets the max value/index of the attribute as the rank of lens.
  *  `use_lens` sets the max value/index as the corresponding value in lens at the axes index.
+ *      Uses the dynamic_dimension.max value for dynamic shapes. Returns the original vector
+ *      (no normalization) if any of dynamic_dimension[axes] are not fixed.
  * 3. `clip_min` vs. `not_clip_min` (default):
  *  Clip values less than the minimum to the minimum or not.
  * 4. `include_min` vs. `exclude_min` (default):

src/include/migraphx/op/pooling.hpp

@@ -70,7 +70,8 @@ struct pooling
     // 2 smaller than the input tensor rank (NCHW layout)
     std::vector<std::size_t> lengths = {1, 1};
 
-    // Dilations are not supported at this time.
+    // Spacing between the elements of the pooling kernel. Must be the same ndim as lengths.
+    std::vector<std::size_t> dilations = {1, 1};
 
     // ceiling mode is a flag affecting output size
     // or equivalently, placements of the pooling kernel.
@@ -99,6 +100,7 @@ struct pooling
                     f(self.padding_mode, "padding_mode"),
                     f(self.stride, "stride"),
                     f(self.lengths, "lengths"),
+                    f(self.dilations, "dilations"),
                     f(self.ceil_mode, "ceil_mode"),
                     f(self.lp_order, "lp_order"),
                     f(self.dyn_global, "dyn_global"));
@@ -112,14 +114,17 @@ struct pooling
             return;
         if((padding_mode != default_ and padding.size() != stride.size() and
             (padding.size()) != stride.size() * 2) or
-           stride.size() != lengths.size())
+           stride.size() != lengths.size() or dilations.size() != lengths.size())
         {
             MIGRAPHX_THROW("POOLING: inconsistent attribute sizes");
         }
-        if(std::any_of(lengths.begin(), lengths.end(), [&](auto i) { return (i == 0); }) or
-           std::any_of(stride.begin(), stride.end(), [&](auto i) { return (i == 0); }))
+
+        const auto is_zero = [](auto el) { return el == 0; };
+        if(std::any_of(lengths.begin(), lengths.end(), is_zero) or
+           std::any_of(stride.begin(), stride.end(), is_zero) or
+           std::any_of(dilations.begin(), dilations.end(), is_zero))
         {
-            MIGRAPHX_THROW("POOLING: size 0 pooling kernel or stride");
+            MIGRAPHX_THROW("POOLING: size 0 pooling kernel or stride or dilations");
         }
 
         // TODO:  update lowering to run the reference
@@ -142,6 +147,11 @@ struct pooling
 
     value attributes() const { return {{"normalize_padding", "padding"}}; }
 
+    inline std::size_t dilate_dim(std::size_t dim, std::size_t dilation) const
+    {
+        return 1 + dilation * (dim - 1);
+    }
+
     std::vector<std::size_t> calc_spatial_dim_out(const std::vector<std::size_t>& input_lens,
                                                   std::size_t kdims) const
     {
@@ -151,8 +161,9 @@ struct pooling
             std::size_t padding_factor = 2 * padding[i];
             if(padding.size() == 2 * kdims)
                 padding_factor = padding[i] + padding[i + kdims];
+            std::size_t dilated_length = dilate_dim(lengths[i], dilations[i]);
             std::size_t dim_size;
-            if(input_lens[i + 2] + padding_factor < lengths[i])
+            if(input_lens[i + 2] + padding_factor < dilated_length)
             {
                 if(padding_mode == default_)
                     MIGRAPHX_THROW("POOLING: not enough padding for the given kernel size");
@@ -162,7 +173,7 @@ struct pooling
             }
             else
             {
-                dim_size = input_lens[i + 2] + padding_factor - lengths[i];
+                dim_size = input_lens[i + 2] + padding_factor - dilated_length;
             }
             std::size_t len =
                 (ceil_mode)
@@ -331,6 +342,7 @@ struct pooling
                 int start = static_cast<int>(idx_o[dim] * stride[d_2]) -
                             static_cast<int>(padding_vals[d_2]);
                 int end;
+                std::size_t dilated_kernel_dim = dilate_dim(kernel_dims[d_2], dilations[d_2]);
                 // NOLINT
                 if(count_include_pad and ceil_mode and (mode != pooling_mode::max))
                 {
@@ -340,15 +352,14 @@ struct pooling
                     // padding.  Clip out-of-bounds indexes but not padding.
 
                     // Check if this kernel extends beyond the padding at end of dimension
-                    end = std::min(start + kernel_dims[d_2],
+                    end = std::min(start + dilated_kernel_dim,
                                    in_lens[dim] + static_cast<int>(padding_vals[d_2]));
                 }
                 else
                 {
                     // In non-ceiling mode, when
                     // count_include_pad is false, or for max pooling, clip off padding.
-                    end   = std::min(start + kernel_dims[d_2], in_lens[dim]);
-                    start = std::max(start, 0);
+                    end = std::min(start + dilated_kernel_dim, in_lens[dim]);
                 }
                 win_start.push_back(start);
                 if(end < start)
@@ -366,6 +377,16 @@ struct pooling
 
             // for each element in the window...
             shape_for_each(win_shape, [&](const auto& idx_w) {
+                // Skip elements that belong to the dilated area
+                for(size_t axis = 0; axis < idx_w.size(); ++axis)
+                {
+                    if(idx_w[axis] % dilations[axis])
+                    {
+                        pool_size -= 1;
+                        return;
+                    }
+                }
+
                 // the coordinates of this element
                 auto idx = idx_o;
 
@@ -390,7 +411,15 @@ struct pooling
                     // this is a padding element.  Padding locations
                     // don't contribute to average or max pooling total but can play in
                     // lpnorm pooling.
-                    output_val = op(output_val, 0);
+                    if(mode == pooling_mode::lpnorm)
+                    {
+                        output_val = op(output_val, op.template init<Type>());
+                    }
+                    if(mode == pooling_mode::average)
+                    {
+                        // Ignore padding
+                        pool_size -= 1;
+                    }
                 }
             });
             output[i] = Type(op.final(output_val, pool_size));

src/include/migraphx/op/prefix_scan_op.hpp

@@ -22,6 +22,12 @@
  * THE SOFTWARE.
  */
 
+/**
+ * Parent struct for prefix scan ops.  A prefix scan is a mathematical entity useful
+ * in parallelizing various computations.  Given a list of numbers, a prefix scan
+ * op returns an equal size list of running totals of the values.  Other operations
+ * besides addition can be supported by child ops.
+ */
 #ifndef MIGRAPHX_GUARD_OPERATORS_SCAN_OP_HPP
 #define MIGRAPHX_GUARD_OPERATORS_SCAN_OP_HPP
 

src/include/migraphx/op/quantizelinear.hpp

@@ -30,11 +30,11 @@
 #include <migraphx/par_for.hpp>
 #include <migraphx/value.hpp>
 #include <cmath>
+#include <fenv.h>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
-
 struct quantizelinear
 {
     std::string name() const { return "quantizelinear"; }
@@ -71,26 +71,26 @@ struct quantizelinear
         {
             y_zero_point = args.at(2);
         }
-
         argument result{output_shape};
+        auto rounding_mode = fegetround();
+        fesetround(FE_TONEAREST);
         visit_all(result, y_zero_point)([&](auto output, auto zero_pts) {
             visit_all(x, y_scale)([&](auto input, auto scales) {
                 using quant_type = typename decltype(output)::value_type;
                 auto min_value   = std::numeric_limits<quant_type>::min();
                 auto max_value   = std::numeric_limits<quant_type>::max();
                 par_for(output_shape.elements(), [&](auto i) {
-                    int64_t quantized = static_cast<int64_t>(std::round(input[i] / scales[i])) +
+                    int64_t quantized = static_cast<int64_t>(std::nearbyint(input[i] / scales[i])) +
                                         static_cast<int64_t>(zero_pts[i]);
                     output[i] = std::max(static_cast<int64_t>(min_value),
                                          std::min(static_cast<int64_t>(max_value), quantized));
                 });
             });
         });
-
+        fesetround(rounding_mode);
         return result;
     }
 };
-
 } // namespace op
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx

src/include/migraphx/op/random_uniform.hpp

@@ -65,11 +65,10 @@ struct random_uniform
         return inputs.at(1);
     }
 
-    argument compute(const shape&, std::vector<argument> args) const
+    argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
         // Output goes into the passed buffer, not the shape output.
-        auto result = args[1];
-
+        argument result{dyn_out.computed_shape};
         uint64_t local_seed = args[0].at<uint64_t>(0);
         std::mt19937 gen(local_seed);
 

src/targets/gpu/include/migraphx/gpu/pack_int8_args.hpp → src/include/migraphx/op/scatternd_max.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -21,25 +21,26 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_PACK_INT8_ARGS_HPP
-#define MIGRAPHX_GUARD_RTGLIB_PACK_INT8_ARGS_HPP
+#ifndef MIGRAPHX_GUARD_OPERATORS_SCATTERND_MAX_HPP
+#define MIGRAPHX_GUARD_OPERATORS_SCATTERND_MAX_HPP
 
-#include <migraphx/program.hpp>
-#include <migraphx/gpu/context.hpp>
+#include <migraphx/op/scatternd_op.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
+namespace op {
 
-namespace gpu {
-
-struct MIGRAPHX_GPU_EXPORT pack_int8_args
+struct scatternd_max : scatternd_op<scatternd_max>
 {
-    std::string name() const { return "gpu::pack_int8_args"; }
-    void apply(module& m) const;
-    shape pack_int8_shape(const shape& s) const;
+    scatternd_max() {}
+
+    auto reduction() const
+    {
+        return [](auto& x, const auto& y) { x = std::max(x, y); };
+    }
 };
 
-} // namespace gpu
+} // namespace op
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/targets/gpu/include/migraphx/gpu/device/gather.hpp → src/include/migraphx/op/scatternd_min.hpp

 /*
  * The MIT License (MIT)
  *
- * Copyright (c) 2015-2022 Advanced Micro Devices, Inc. All rights reserved.
+ * Copyright (c) 2015-2023 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
@@ -21,23 +21,26 @@
  * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
  * THE SOFTWARE.
  */
-#ifndef MIGRAPHX_GUARD_RTGLIB_DEVICE_GATHER_HPP
-#define MIGRAPHX_GUARD_RTGLIB_DEVICE_GATHER_HPP
+#ifndef MIGRAPHX_GUARD_OPERATORS_SCATTERND_MIN_HPP
+#define MIGRAPHX_GUARD_OPERATORS_SCATTERND_MIN_HPP
 
-#include <migraphx/argument.hpp>
-#include <migraphx/gpu/device/config.hpp>
-#include <hip/hip_runtime_api.h>
+#include <migraphx/op/scatternd_op.hpp>
 
 namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
-namespace gpu {
-namespace device {
+namespace op {
 
-argument MIGRAPHX_DEVICE_EXPORT
-gather(hipStream_t stream, argument result, argument arg1, argument arg2, int64_t axis);
+struct scatternd_min : scatternd_op<scatternd_min>
+{
+    scatternd_min() {}
 
-} // namespace device
-} // namespace gpu
+    auto reduction() const
+    {
+        return [](auto& x, const auto& y) { x = std::min(x, y); };
+    }
+};
+
+} // namespace op
 } // namespace MIGRAPHX_INLINE_NS
 } // namespace migraphx
 

src/include/migraphx/op/scatternd_op.hpp

@@ -121,7 +121,8 @@ struct scatternd_op : op_name<Derived>
                 auto k             = indices_shape.lens().back();
                 auto q             = indices_shape.ndim();
                 auto r             = dyn_out.computed_shape.ndim();
-                par_for(updates_shape.elements(), [&](const auto i) {
+                for(auto i = 0u; i < updates_shape.elements(); ++i)
+                {
                     auto updates_idx = updates_std.multi(i);
                     std::vector<std::size_t> indices_idx(q, 0);
                     std::copy(
@@ -135,7 +136,7 @@ struct scatternd_op : op_name<Derived>
                     std::copy(updates_idx.begin() + q - 1, updates_idx.end(), out_idx.begin() + k);
 
                     self.reduction()(output[dyn_out.computed_shape.index(out_idx)], updates[i]);
-                });
+                }
             });
         });