Merge branch 'develop' into dependabot/pip/test/py/numpy-1.22.0

src/driver/argument_parser.hpp

@@ -187,6 +187,13 @@ struct value_parser
     }
 };
 
+// version for std::optional object
+template <class T>
+struct value_parser<std::optional<T>>
+{
+    static T apply(const std::string& x) { return value_parser<T>::apply(x); }
+};
+
 struct argument_parser
 {
     struct argument

src/driver/main.cpp

@@ -540,22 +540,17 @@ struct params : command<params>
 struct verify : command<verify>
 {
     compiler c;
-    // Set to -1. as nonsense initial value
-    double rms_tol       = -1.0;
-    double atol          = -1.0;
-    double rtol          = -1.0;
+    std::optional<double> rms_tol;
+    std::optional<double> atol;
+    std::optional<double> rtol;
     bool per_instruction = false;
     bool reduce          = false;
     void parse(argument_parser& ap)
     {
         c.parse(ap);
-        ap(rms_tol, {"--rms-tol"}, ap.help("Tolerance for the RMS error (Default: 0.001)"));
-        ap(atol,
-           {"--atol"},
-           ap.help("Tolerance for the elementwise absolute difference (Default: 0.001)"));
-        ap(rtol,
-           {"--rtol"},
-           ap.help("Tolerance for the elementwise relative difference (Default: 0.001)"));
+        ap(rms_tol, {"--rms-tol"}, ap.help("Tolerance for the RMS error"));
+        ap(atol, {"--atol"}, ap.help("Tolerance for the elementwise absolute difference"));
+        ap(rtol, {"--rtol"}, ap.help("Tolerance for the elementwise relative difference"));
         ap(per_instruction,
            {"-i", "--per-instruction"},
            ap.help("Verify each instruction"),
@@ -572,33 +567,6 @@ struct verify : command<verify>
         auto t = c.ct.get_target();
         auto m = c.parameters.generate(p, t, true, c.l.batch);
 
-        // TODO remove this and make the driver able to figure out datatype most used in the model
-        //  then set the tolerances appropriately. Need to check here because c.to_fp16 only set
-        //  after argument_parser.parse() is run. This code is complicated because there's not a
-        //  good way to change the default tolerances after reading `--fp16` but before reading
-        //  `--rms-tol`, `--atol`, and `--rtol`.
-        migraphx::verify::tolerance tols{};
-        if(c.to_fp16)
-        {
-            tols = migraphx::verify::tolerance{8e-2, 4e-2, 4e-2};
-        }
-        if(not float_equal(this->rms_tol, -1.0))
-        {
-            tols.rms_tol = this->rms_tol;
-        }
-        if(not float_equal(this->atol, -1.0))
-        {
-            tols.atol = this->atol;
-        }
-        if(not float_equal(this->rtol, -1.0))
-        {
-            tols.rtol = this->rtol;
-        }
-
-        std::cout << "rms_tol: " << tols.rms_tol << std::endl;
-        std::cout << "atol: " << tols.atol << std::endl;
-        std::cout << "rtol: " << tols.rtol << std::endl;
-
         auto quantize = precision::fp32;
         if(c.to_fp16)
         {
@@ -609,6 +577,11 @@ struct verify : command<verify>
             quantize = precision::int8;
         }
 
+        auto tols = get_tolerances(p, quantize, rms_tol, atol, rtol);
+        std::cout << "rms_tol: " << tols.rms_tol << std::endl;
+        std::cout << "atol: " << tols.atol << std::endl;
+        std::cout << "rtol: " << tols.rtol << std::endl;
+
         if(per_instruction)
         {
             verify_instructions(p, t, c.co, quantize, tols);

src/driver/verify.cpp

@@ -36,6 +36,42 @@ namespace migraphx {
 namespace driver {
 inline namespace MIGRAPHX_INLINE_NS {
 
+/**
+ * Gives tolerances based on user input (`rms_tol`, `atol`, `rtol` parameters) and defaults.
+ * Sets to fp16 tolerances if `quantize` input is fp16 or any fp16 instruction in found in the
+ * model.
+ */
+verify::tolerance get_tolerances(const program& p,
+                                 precision quantize,
+                                 std::optional<double> rms_tol,
+                                 std::optional<double> atol,
+                                 std::optional<double> rtol)
+{
+    bool has_fp16 = any_of(p.get_modules(), [](auto&& m) {
+        return any_of(*m, [](auto&& ins) { return (ins.get_shape().type() == shape::half_type); });
+    });
+    migraphx::verify::tolerance result{};
+    if(has_fp16 or quantize == precision::fp16)
+    {
+        result.rms_tol = 8e-2;
+        result.atol    = 4e-2;
+        result.rtol    = 4e-2;
+    }
+    if(rms_tol)
+    {
+        result.rms_tol = *rms_tol;
+    }
+    if(atol)
+    {
+        result.atol = *atol;
+    }
+    if(rtol)
+    {
+        result.rtol = *rtol;
+    }
+    return result;
+}
+
 std::vector<argument> run_ref(program p, const parameter_map& inputs)
 {
     p.compile(migraphx::make_target("ref"));

src/driver/verify.hpp

@@ -32,6 +32,12 @@ namespace migraphx {
 namespace driver {
 inline namespace MIGRAPHX_INLINE_NS {
 
+verify::tolerance get_tolerances(const program& p,
+                                 precision quantize,
+                                 std::optional<double> rms_tol,
+                                 std::optional<double> atol,
+                                 std::optional<double> rtol);
+
 void verify_program(const std::string& name,
                     const program& p,
                     const target& t,

src/include/migraphx/op/reshape.hpp

@@ -36,6 +36,22 @@ namespace migraphx {
 inline namespace MIGRAPHX_INLINE_NS {
 namespace op {
 
+/**
+ * 1 input version:
+ * reshape(input_data)
+ * this.dims = output_dims
+ * Makes a copy of input_data to the output shape.
+ *
+ * 2 input version:
+ * reshape(input_data, output_buffer)
+ * this.dims = unset
+ * Copies input_data to output_buffer; output_buffer already has the output shape.
+ * This version will not fail gracefully if the input shape and output_buffer shape are
+ * incompatible. There's a throw that will catch when the number of elements do not match at
+ * runtime. This version should only be used for dynamic reshapes (output dimensions only known at
+ * runtime). If output_buffer has a static shape during compile/parse, you can use the 1 input
+ * version.
+ */
 struct reshape
 {
     std::vector<int64_t> dims;
@@ -215,32 +231,56 @@ struct reshape
 
     shape compute_shape(std::vector<shape> inputs) const
     {
-        check_shapes{inputs, *this, true}.has(1);
+        check_shapes{inputs, *this, true}.has(1, 2);
 
         auto n_neg_dims = std::count(dims.begin(), dims.end(), -1);
         if(n_neg_dims > 1)
             MIGRAPHX_THROW("reshape: Dimensions for reshape can only have one -1 dim");
 
         auto s0 = inputs.front();
-        if(s0.dynamic())
+        if(inputs.size() == 1)
         {
-            return dyn_compute_shape(s0);
+            if(s0.dynamic())
+            {
+                return dyn_compute_shape(s0);
+            }
+            else
+            {
+                return static_compute_shape(inputs, n_neg_dims);
+            }
         }
         else
         {
-            return static_compute_shape(inputs, n_neg_dims);
+            return inputs.back();
         }
     }
 
     argument compute(const dyn_output& dyn_out, std::vector<argument> args) const
     {
         assert(dyn_out.computed_shape.standard());
-        argument result{dyn_out.computed_shape};
+        if(args.size() == 1)
+        {
+            argument result{dyn_out.computed_shape};
 
-        visit_all(result, args[0])([&](auto output, auto input) {
-            std::copy(input.begin(), input.end(), output.begin());
-        });
-        return result;
+            visit_all(result, args[0])([&](auto output, auto input) {
+                std::copy(input.begin(), input.end(), output.begin());
+            });
+            return result;
+        }
+        else
+        {
+            // 2 arg
+            if(args[0].get_shape().elements() != args[1].get_shape().elements())
+            {
+                MIGRAPHX_THROW("Reshape: Number of elements must match at runtime. Input: " +
+                               std::to_string(args[0].get_shape().elements()) +
+                               " Output buffer: " + std::to_string(args[1].get_shape().elements()));
+            }
+            visit_all(args[1], args[0])([&](auto output, auto input) {
+                std::copy(input.begin(), input.end(), output.begin());
+            });
+            return args[1];
+        }
     }
 };
 

src/onnx/parse_groupnorm.cpp

@@ -0,0 +1,130 @@
+/*
+ * The MIT License (MIT)
+ *
+ * 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
+ * in the Software without restriction, including without limitation the rights
+ * to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+ * copies of the Software, and to permit persons to whom the Software is
+ * furnished to do so, subject to the following conditions:
+ *
+ * The above copyright notice and this permission notice shall be included in
+ * all copies or substantial portions of the Software.
+ *
+ * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+ * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+ * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.  IN NO EVENT SHALL THE
+ * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+ * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+ * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+ * THE SOFTWARE.
+ */
+#include <migraphx/onnx/op_parser.hpp>
+#include <migraphx/ranges.hpp>
+#include <migraphx/make_op.hpp>
+#include <migraphx/instruction.hpp>
+
+namespace migraphx {
+inline namespace MIGRAPHX_INLINE_NS {
+namespace onnx {
+
+struct parse_groupnorm : op_parser<parse_groupnorm>
+{
+    std::vector<op_desc> operators() const { return {{"GroupNormalization"}}; }
+
+    instruction_ref parse(const op_desc& /*opd*/,
+                          const onnx_parser& parser,
+                          const onnx_parser::node_info& info,
+                          std::vector<instruction_ref> args) const
+    {
+        float epsilon = 1e-5f;
+        if(contains(info.attributes, "epsilon"))
+        {
+            epsilon = parser.parse_value(info.attributes.at("epsilon")).at<float>();
+        }
+        size_t num_groups;
+        if(contains(info.attributes, "num_groups"))
+        {
+            num_groups = parser.parse_value(info.attributes.at("num_groups")).at<size_t>();
+        }
+        else
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: num_groups must be available");
+        }
+
+        if(args.size() != 3)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: invalid input count");
+        }
+
+        auto x     = args.at(0);
+        auto scale = args.at(1);
+        auto bias  = args.at(2);
+
+        auto x_shape = x->get_shape();
+        auto x_dtype = x_shape.type();
+        auto x_dims  = x_shape.lens();
+
+        if(x_shape.ndim() <= 2)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: invalid input shape");
+        }
+
+        auto c = x_shape.lens().at(1);
+        if(c % num_groups != 0)
+        {
+            MIGRAPHX_THROW(
+                "PARSE_GROUPNORM: num_groups should be a divisor of the number of channels");
+        }
+        auto group_size = c / num_groups;
+        if(scale->get_shape().ndim() != 1 or scale->get_shape().lens().at(0) != num_groups)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: scale tensor shape should be num_groups");
+        }
+        if(bias->get_shape().ndim() != 1 or bias->get_shape().lens().at(0) != num_groups)
+        {
+            MIGRAPHX_THROW("PARSE_GROUPNORM: bias tensor shape should be num_groups");
+        }
+
+        // Original shape: N x C x D1 x ... x Dn
+        // New shape: N x num_groups x C // num_groups x D1 x ... x Dn
+
+        std::vector<size_t> dims = {x_dims.at(0), num_groups, group_size};
+        std::copy(x_dims.begin() + 2, x_dims.end(), std::back_inserter(dims));
+        auto x_reshaped = info.add_instruction(make_op("reshape", {{"dims", dims}}), x);
+
+        // Axes for D1 x ... x Dn
+        std::vector<size_t> axes(dims.size() - 2);
+        std::iota(axes.begin(), axes.end(), 2);
+
+        // y = (x - mean) * rsqrt(variance + epsilon) * scale + bias
+        // mean = reduce_mean({D1, D2, ... Dk}, x)
+        // variance = reduce_mean({D1, D2, ... Dk}, (x - mean)^2)
+
+        auto mean = info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), x_reshaped);
+        auto x_sub_mean    = info.add_common_op("sub", x_reshaped, mean);
+        auto x_sqdiff_mean = info.add_common_op("sqdiff", x_reshaped, mean);
+        auto variance =
+            info.add_instruction(make_op("reduce_mean", {{"axes", axes}}), x_sqdiff_mean);
+        epsilon =
+            (x_dtype == migraphx::shape::half_type and std::abs(epsilon) < 1e-7) ? 1e-7 : epsilon;
+        auto eps     = info.add_literal(migraphx::literal{migraphx::shape{x_dtype}, {epsilon}});
+        auto var_eps = info.add_common_op("add", variance, eps);
+        auto rsqrt   = info.add_instruction(make_op("rsqrt"), var_eps);
+        auto result  = info.add_common_op("mul", x_sub_mean, rsqrt);
+        auto scale_bcast =
+            info.add_instruction(make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), scale);
+        auto bias_bcast =
+            info.add_instruction(make_op("broadcast", {{"axis", 1}, {"out_lens", dims}}), bias);
+        auto scaled     = info.add_instruction(make_op("mul"), result, scale_bcast);
+        auto y          = info.add_instruction(make_op("add"), scaled, bias_bcast);
+        auto y_reshaped = info.add_instruction(make_op("reshape", {{"dims", x_dims}}), y);
+        return y_reshaped;
+    }
+};
+
+} // namespace onnx
+} // namespace MIGRAPHX_INLINE_NS
+} // namespace migraphx