v3.9 (#2185)

* v3.8 update x * fix blackwell gg * doc change * doc change * doc change --------- Co-authored-by: yuzhai <yuzhai@nvidia.com> Co-authored-by: Haicheng Wu <haichengw@nvidia.com> Co-authored-by: Haicheng Wu <57973641+hwu36@users.noreply.github.com>
2025-03-20 22:52:23 -07:00
parent 8c4d1dc47d
commit 62750a2b75
334 changed files with 91517 additions and 2656 deletions
--- a/examples/cute/tutorial/blackwell/example_utils.hpp
+++ b/examples/cute/tutorial/blackwell/example_utils.hpp
@ -0,0 +1,105 @@
+/***************************************************************************************************
+ * Copyright (c) 2024 - 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+ * SPDX-License-Identifier: BSD-3-Clause
+ *
+ * Redistribution and use in source and binary forms, with or without
+ * modification, are permitted provided that the following conditions are met:
+ *
+ * 1. Redistributions of source code must retain the above copyright notice, this
+ * list of conditions and the following disclaimer.
+ *
+ * 2. Redistributions in binary form must reproduce the above copyright notice,
+ * this list of conditions and the following disclaimer in the documentation
+ * and/or other materials provided with the distribution.
+ *
+ * 3. Neither the name of the copyright holder nor the names of its
+ * contributors may be used to endorse or promote products derived from
+ * this software without specific prior written permission.
+ *
+ * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+ * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+ * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+ * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+ * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+ * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+ * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+ * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+ * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+ * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+ *
+ **************************************************************************************************/
+
+#pragma once
+#include <cute/tensor.hpp>                    // CuTe tensor implementation
+#include <cute/arch/copy_sm90_desc.hpp>
+
+template <class AccType,
+          class TensorA, class TensorB,
+          class TensorC, class TensorD,
+          class Alpha, class Beta>
+void
+reference_gemm(TensorA const& tensor_A, TensorB const& tensor_B,
+               TensorC const& tensor_C, TensorD      & tensor_D,
+               Alpha alpha, Beta beta)
+{
+  using namespace cute;
+  for (int m = 0; m < size<0>(tensor_D); ++m) {
+    for (int n = 0; n < size<1>(tensor_D); ++n) {
+      AccType c = AccType(0.f);
+      for (int k = 0; k < size<1>(tensor_A); ++k) {
+        c += tensor_A(m,k) * tensor_B(n,k);
+      }
+      tensor_D(m,n) = alpha * c + beta * tensor_C(m,n);
+    }
+  }
+}
+
+template <class TensorA, class TensorB,
+          class TensorC, class TensorD,
+          class RefTensorD>
+bool
+compare_results(TensorA const& tensor_A, TensorB const& tensor_B,
+                TensorC const& tensor_C, TensorD const& tensor_D,
+                RefTensorD const& ref_tensor_D,
+                bool print_diff = false)
+{
+  using namespace cute;
+  auto norm_A = matrix_inf_norm(tensor_A);
+  auto norm_B = matrix_inf_norm(tensor_B);
+  auto norm_C = matrix_inf_norm(tensor_C);
+  auto norm_D = matrix_inf_norm(tensor_D);
+  auto norm_ref_D = matrix_inf_norm(ref_tensor_D);
+  auto norm_diff = matrix_diff_inf_norm(tensor_D, ref_tensor_D);
+
+  if (print_diff) {
+    for (int m = 0; m < size<0>(tensor_D); ++m) {
+      for (int n = 0; n < size<1>(tensor_D); ++n) {
+        std::cout << m << "," << n << " : " << tensor_D(m,n) << " vs. " << ref_tensor_D(m,n) << std::endl;
+      }
+    }
+  }
+
+  std::cout << "norm (A)       : " << norm_A.inf_norm << std::endl;
+  std::cout << "norm (B)       : " << norm_B.inf_norm << std::endl;
+  std::cout << "norm (C)       : " << norm_C.inf_norm << std::endl;
+  std::cout << "norm (D)       : " << norm_D.inf_norm << std::endl;
+  std::cout << "norm (ref_D)   : " << norm_ref_D.inf_norm << std::endl;
+  std::cout << "norm (D-ref_D) : " << norm_diff.inf_norm << std::endl;
+
+  return (!norm_A.found_nan) && (!norm_B.found_nan) &&
+         (!norm_C.found_nan) && (!norm_D.found_nan) && (!norm_ref_D.found_nan) &&                 // There are no NaNs
+         (norm_A.inf_norm > 0.0) && (norm_B.inf_norm > 0.0) &&
+         (norm_C.inf_norm > 0.0) && (norm_D.inf_norm > 0.0) && (norm_ref_D.inf_norm > 0.0) &&     // Values in tensors aren't zeros
+         (norm_diff.inf_norm <= 0.0);                                                             // Diff (ref_D-D) == 0
+}
+
+template <class Tensor>
+void
+initialize_tensor(Tensor& tensor, cute::tuple<int, int> value_range = {-2, 2})
+{
+  using DataType = typename Tensor::element_type;
+  auto [min, max] = value_range;
+  for (int i = 0; i < cute::size(tensor); i++) {
+    tensor(i) = DataType(int((max-min)*(rand() / double(RAND_MAX)) + min));
+  }
+}