From 0b834caaceae2076979907c24dc65e188739603d Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Tue, 4 May 2021 15:22:05 +0200
Subject: [PATCH 1/7] add gemm function

---
 mlprodict/testing/direct_blas_lapack.pyx | 67 ++++++++++++++++++++++++
 setup.py                                 |  9 ++++
 2 files changed, 76 insertions(+)
 create mode 100644 mlprodict/testing/direct_blas_lapack.pyx

diff --git a/mlprodict/testing/direct_blas_lapack.pyx b/mlprodict/testing/direct_blas_lapack.pyx
new file mode 100644
index 000000000..373a1c72f
--- /dev/null
+++ b/mlprodict/testing/direct_blas_lapack.pyx
@@ -0,0 +1,67 @@
+"""
+@file
+@brief Direct calls to libraries :epkg:`BLAS` and :epkg:`LAPACK`.
+"""
+from libc.stdio cimport printf
+
+import numpy
+cimport numpy
+cimport cython
+numpy.import_array()
+# cimport scipy.linalg.cython_lapack as cython_lapack
+cimport scipy.linalg.cython_blas as cython_blas
+
+
+
+cdef void dgemm_dot(numpy.ndarray[double, ndim=2, mode='c'] A,
+                    numpy.ndarray[double, ndim=2, mode='c'] B,
+                    int transA, int transB,
+                    numpy.ndarray[double, ndim=2, mode='c'] C):
+    """
+    Calls gemm for a dot product. Avoids translation if possible.
+    Does `A @ B`.
+    """
+
+    cdef:
+        char ca = "T" if transA else "N"
+        char cb = "T" if transB else "N"
+        int lda = K if transA else M
+        int ldb = K if transB else N
+        int ldc = 0
+        const double* pa = &A[0, 0]
+        const double* pb = &B[0, 0]
+        double* pc = &C[0, 0]
+        int M = A.shape[1] if transA else A.shape[0]
+        int N = B.shape[0] if transB else B.shape[0]
+        int K = A.shape[0] if transA else A.shape[1]
+        double one = 1.
+        double zero = 0.
+
+    cython_blas.dgemm(&ca, &cb, &M, &N, &K, &one, pa, &lda, pb, &ldb, &zero, pb, &ldc)
+
+
+cdef void sgemm_dot(numpy.ndarray[float, ndim=2, mode='c'] A,
+                    numpy.ndarray[float, ndim=2, mode='c'] B,
+                    int transA, int transB,
+                    numpy.ndarray[float, ndim=2, mode='c'] C):
+    """
+    Calls gemm for a dot product. Avoids translation if possible.
+    Does `A @ B`.
+    """
+
+    cdef:
+        char ca = "T" if transA else "N"
+        char cb = "T" if transB else "N"
+        int lda = K if transA else M
+        int ldb = K if transB else N
+        int ldc = 0
+        const float* pa = &A[0, 0]
+        const float* pb = &B[0, 0]
+        float* pc = &C[0, 0]
+        int M = A.shape[1] if transA else A.shape[0]
+        int N = B.shape[0] if transB else B.shape[0]
+        int K = A.shape[0] if transA else A.shape[1]
+        float one = 1.
+        float zero = 0.
+
+    cython_blas.sgemm(&ca, &cb, &M, &N, &K, &one, pa, &lda, pb, &ldb, &zero, pb, &ldc)
diff --git a/setup.py b/setup.py
index f8074f44c..25b229d2f 100644
--- a/setup.py
+++ b/setup.py
@@ -87,6 +87,8 @@ def get_compile_args():
 
 
 def get_extensions():
+    import numpy
+
     root = os.path.abspath(os.path.dirname(__file__))
     (libraries_thread, extra_compile_args,
      extra_link_args, define_macros) = get_compile_args()
@@ -294,7 +296,14 @@ def get_extensions():
         define_macros=define_macros,
         language='c++')
 
+    cython_extensions = ["direct_blas_lapack"]
+    ext_blas = Extension("mlprodict.testing.direct_blas_lapack",
+                  ['mlprodict/testing/direct_blas_lapack.pyx'],
+                  include_dirs=[numpy.get_include()],
+                  language='c')
+
     ext_modules = [
+        ext_blas,
         ext_conv,
         ext_conv_transpose,
         ext_experimental_c,

From e2854229cc56affb2975bf0fecb72489e083953e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Wed, 5 May 2021 13:29:18 +0200
Subject: [PATCH 2/7] use gemm in the ONNX graph

---
 _doc/notebooks/einsum_decomposition.ipynb | 394 +++++++++++-----------
 _unittests/ut_testing/test_blas_lapack.py | 280 +++++++++++++++
 _unittests/ut_testing/test_einsum.py      |  27 +-
 mlprodict/onnxrt/ops_cpu/op_gemm.py       |   4 +-
 mlprodict/testing/blas_lapack.py          | 199 +++++++++++
 mlprodict/testing/direct_blas_lapack.pyx  | 113 ++++---
 mlprodict/testing/einsum_bench.py         |   3 +-
 mlprodict/testing/einsum_impl_classes.py  | 108 ++++--
 setup.py                                  |  14 +-
 9 files changed, 876 insertions(+), 266 deletions(-)
 create mode 100644 _unittests/ut_testing/test_blas_lapack.py
 create mode 100644 mlprodict/testing/blas_lapack.py

diff --git a/_doc/notebooks/einsum_decomposition.ipynb b/_doc/notebooks/einsum_decomposition.ipynb
index cfe71d05a..1ca842418 100644
--- a/_doc/notebooks/einsum_decomposition.ipynb
+++ b/_doc/notebooks/einsum_decomposition.ipynb
@@ -294,16 +294,16 @@
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-              "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\n  ranksep=0.25;\\n  nodesep=0.05;\\n  size=None;\\n  orientation=portrait;\\n\\n  X1 [shape=box color=red label=\\\"X1\\nfloat((0, 0, 0))\\\" fontsize=10];\\n  X2 [shape=box color=red label=\\\"X2\\nfloat((0, 0))\\\" fontsize=10];\\n  X3 [shape=box color=red label=\\\"X3\\nfloat((0, 0, 0))\\\" fontsize=10];\\n\\n  Y [shape=box color=green label=\\\"Y\\nfloat((0, 0, 0))\\\" fontsize=10];\\n\\n  einsum1800417238368_id_axes [shape=box label=\\\"einsum1800417238368_id_axes\\nint64((3,))\\n[3 4 5]\\\" fontsize=10];\\n  einsum1800417281120_re_axes [shape=box label=\\\"einsum1800417281120_re_axes\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum1800417281072_id_axes [shape=box label=\\\"einsum1800417281072_id_axes\\nint64((4,))\\n[0 1 4 5]\\\" fontsize=10];\\n  einsum1800417282080_ba_1 [shape=box label=\\\"einsum1800417282080_ba_1\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum1800417282080_ba_0 [shape=box label=\\\"einsum1800417282080_ba_0\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum1800417282080_ba_batch_axes [shape=box label=\\\"einsum1800417282080_ba_batch_axes\\nint64((4,))\\n[0 1 2 3]\\\" fontsize=10];\\n  einsum1800417282080_ba__1 [shape=box label=\\\"einsum1800417282080_ba__1\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum1800417282080_ba_left_set [shape=box label=\\\"einsum1800417282080_ba_left_set\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum1800417282080_ba_right_set [shape=box label=\\\"einsum1800417282080_ba_right_set\\nint64((1,))\\n[5]\\\" fontsize=10];\\n  einsum1800417281840_id_axes [shape=box label=\\\"einsum1800417281840_id_axes\\nint64((3,))\\n[0 1 2]\\\" fontsize=10];\\n  einsum1800417281888_re_axes [shape=box label=\\\"einsum1800417281888_re_axes\\nint64((1,))\\n[5]\\\" fontsize=10];\\n  einsum1800417282704_ba_1 [shape=box label=\\\"einsum1800417282704_ba_1\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum1800417282704_ba_0 [shape=box label=\\\"einsum1800417282704_ba_0\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum1800417282704_ba_batch_axes [shape=box label=\\\"einsum1800417282704_ba_batch_axes\\nint64((2,))\\n[0 1]\\\" fontsize=10];\\n  einsum1800417282704_ba_sum_axes [shape=box label=\\\"einsum1800417282704_ba_sum_axes\\nint64((1,))\\n[5]\\\" fontsize=10];\\n  einsum1800417282704_ba__1 [shape=box label=\\\"einsum1800417282704_ba__1\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum1800417282704_ba_left_set [shape=box label=\\\"einsum1800417282704_ba_left_set\\nint64((2,))\\n[2 3]\\\" fontsize=10];\\n  einsum1800417282704_ba_right_set [shape=box label=\\\"einsum1800417282704_ba_right_set\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum1800417282704_ba_ones [shape=box label=\\\"einsum1800417282704_ba_ones\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum1800417282416_tr_axes [shape=box label=\\\"einsum1800417282416_tr_axes\\nint64((3,))\\n[0 3 5]\\\" fontsize=10];\\n\\n  einsum1800417238368_id [shape=box label=\\\"einsum1800417238368_id\\\" fontsize=10];\\n  Identity [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity)\\\" fontsize=10];\\n  X1 -> Identity;\\n  Identity -> einsum1800417238368_id;\\n\\n  einsum1800417281600_ex [shape=box label=\\\"einsum1800417281600_ex\\\" fontsize=10];\\n  Unsqueeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze)\\\" fontsize=10];\\n  einsum1800417238368_id -> Unsqueeze;\\n  einsum1800417238368_id_axes -> Unsqueeze;\\n  Unsqueeze -> einsum1800417281600_ex;\\n\\n  einsum1800417281552_tr [shape=box label=\\\"einsum1800417281552_tr\\\" fontsize=10];\\n  Transpose [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose)\\nperm=[1 0 2 3 4 5]\\\" fontsize=10];\\n  einsum1800417281600_ex -> Transpose;\\n  Transpose -> einsum1800417281552_tr;\\n\\n  einsum1800417281120_re [shape=box label=\\\"einsum1800417281120_re\\\" fontsize=10];\\n  ReduceSum [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceSum\\n(ReduceSum)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800417281552_tr -> ReduceSum;\\n  einsum1800417281120_re_axes -> ReduceSum;\\n  ReduceSum -> einsum1800417281120_re;\\n\\n  einsum1800417281072_id [shape=box label=\\\"einsum1800417281072_id\\\" fontsize=10];\\n  Identity1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity1)\\\" fontsize=10];\\n  X2 -> Identity1;\\n  Identity1 -> einsum1800417281072_id;\\n\\n  einsum1800417280352_ex [shape=box label=\\\"einsum1800417280352_ex\\\" fontsize=10];\\n  Unsqueeze1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze1)\\\" fontsize=10];\\n  einsum1800417281072_id -> Unsqueeze1;\\n  einsum1800417281072_id_axes -> Unsqueeze1;\\n  Unsqueeze1 -> einsum1800417280352_ex;\\n\\n  einsum1800417281360_tr [shape=box label=\\\"einsum1800417281360_tr\\\" fontsize=10];\\n  Transpose1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1)\\nperm=[0 2 4 5 1 3]\\\" fontsize=10];\\n  einsum1800417281120_re -> Transpose1;\\n  Transpose1 -> einsum1800417281360_tr;\\n\\n  einsum1800417281216_tr [shape=box label=\\\"einsum1800417281216_tr\\\" fontsize=10];\\n  Transpose12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose12)\\nperm=[0 2 4 5 1 3]\\\" fontsize=10];\\n  einsum1800417280352_ex -> Transpose12;\\n  Transpose12 -> einsum1800417281216_tr;\\n\\n  einsum1800417282080_ba_shape1 [shape=box label=\\\"einsum1800417282080_ba_shape1\\\" fontsize=10];\\n  Shape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape)\\\" fontsize=10];\\n  einsum1800417281360_tr -> Shape;\\n  Shape -> einsum1800417282080_ba_shape1;\\n\\n  einsum1800417282080_ba_shape2 [shape=box label=\\\"einsum1800417282080_ba_shape2\\\" fontsize=10];\\n  Shape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape1)\\\" fontsize=10];\\n  einsum1800417281216_tr -> Shape1;\\n  Shape1 -> einsum1800417282080_ba_shape2;\\n\\n  einsum1800417282080_ba_dim0g [shape=box label=\\\"einsum1800417282080_ba_dim0g\\\" fontsize=10];\\n  Gather [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather)\\\" fontsize=10];\\n  einsum1800417282080_ba_shape1 -> Gather;\\n  einsum1800417282080_ba_batch_axes -> Gather;\\n  Gather -> einsum1800417282080_ba_dim0g;\\n\\n  einsum1800417282080_ba_dim0bg [shape=box label=\\\"einsum1800417282080_ba_dim0bg\\\" fontsize=10];\\n  Gather1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1)\\\" fontsize=10];\\n  einsum1800417282080_ba_shape2 -> Gather1;\\n  einsum1800417282080_ba_batch_axes -> Gather1;\\n  Gather1 -> einsum1800417282080_ba_dim0bg;\\n\\n  einsum1800417282080_ba_dim0 [shape=box label=\\\"einsum1800417282080_ba_dim0\\\" fontsize=10];\\n  ReduceProd [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800417282080_ba_dim0g -> ReduceProd;\\n  ReduceProd -> einsum1800417282080_ba_dim0;\\n\\n  einsum1800417282080_ba_dim0b [shape=box label=\\\"einsum1800417282080_ba_dim0b\\\" fontsize=10];\\n  ReduceProd1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd1)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800417282080_ba_dim0bg -> ReduceProd1;\\n  ReduceProd1 -> einsum1800417282080_ba_dim0b;\\n\\n  einsum1800417282080_ba_resh1 [shape=box label=\\\"einsum1800417282080_ba_resh1\\\" fontsize=10];\\n  Concat [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat)\\naxis=0\\\" fontsize=10];\\n  einsum1800417282080_ba_dim0 -> Concat;\\n  einsum1800417282080_ba__1 -> Concat;\\n  einsum1800417282080_ba_1 -> Concat;\\n  Concat -> einsum1800417282080_ba_resh1;\\n\\n  einsum1800417282080_ba_resh2 [shape=box label=\\\"einsum1800417282080_ba_resh2\\\" fontsize=10];\\n  Concat1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat1)\\naxis=0\\\" fontsize=10];\\n  einsum1800417282080_ba_dim0b -> Concat1;\\n  einsum1800417282080_ba__1 -> Concat1;\\n  einsum1800417282080_ba_1 -> Concat1;\\n  Concat1 -> einsum1800417282080_ba_resh2;\\n\\n  einsum1800417282080_ba_aresh1 [shape=box label=\\\"einsum1800417282080_ba_aresh1\\\" fontsize=10];\\n  Reshape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape)\\\" fontsize=10];\\n  einsum1800417281360_tr -> Reshape;\\n  einsum1800417282080_ba_resh1 -> Reshape;\\n  Reshape -> einsum1800417282080_ba_aresh1;\\n\\n  einsum1800417282080_ba_aresh2 [shape=box label=\\\"einsum1800417282080_ba_aresh2\\\" fontsize=10];\\n  Reshape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape1)\\\" fontsize=10];\\n  einsum1800417281216_tr -> Reshape1;\\n  einsum1800417282080_ba_resh2 -> Reshape1;\\n  Reshape1 -> einsum1800417282080_ba_aresh2;\\n\\n  einsum1800417282080_ba_aresh2_tr [shape=box label=\\\"einsum1800417282080_ba_aresh2_tr\\\" fontsize=10];\\n  Transpose123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose123)\\nperm=[0 2 1]\\\" fontsize=10];\\n  einsum1800417282080_ba_aresh2 -> Transpose123;\\n  Transpose123 -> einsum1800417282080_ba_aresh2_tr;\\n\\n  einsum1800417282080_ba_dot [shape=box label=\\\"einsum1800417282080_ba_dot\\\" fontsize=10];\\n  MatMul [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"MatMul\\n(MatMul)\\\" fontsize=10];\\n  einsum1800417282080_ba_aresh1 -> MatMul;\\n  einsum1800417282080_ba_aresh2_tr -> MatMul;\\n  MatMul -> einsum1800417282080_ba_dot;\\n\\n  einsum1800417282080_ba_max_dim [shape=box label=\\\"einsum1800417282080_ba_max_dim\\\" fontsize=10];\\n  Max [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Max\\n(Max)\\\" fontsize=10];\\n  einsum1800417282080_ba_dim0g -> Max;\\n  einsum1800417282080_ba_dim0bg -> Max;\\n  Max -> einsum1800417282080_ba_max_dim;\\n\\n  einsum1800417282080_ba_left_dim [shape=box label=\\\"einsum1800417282080_ba_left_dim\\\" fontsize=10];\\n  Gather12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12)\\\" fontsize=10];\\n  einsum1800417282080_ba_shape1 -> Gather12;\\n  einsum1800417282080_ba_left_set -> Gather12;\\n  Gather12 -> einsum1800417282080_ba_left_dim;\\n\\n  einsum1800417282080_ba_right_dim [shape=box label=\\\"einsum1800417282080_ba_right_dim\\\" fontsize=10];\\n  Gather123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123)\\\" fontsize=10];\\n  einsum1800417282080_ba_shape2 -> Gather123;\\n  einsum1800417282080_ba_right_set -> Gather123;\\n  Gather123 -> einsum1800417282080_ba_right_dim;\\n\\n  einsum1800417282080_ba_new_shape [shape=box label=\\\"einsum1800417282080_ba_new_shape\\\" fontsize=10];\\n  Concat12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat12)\\naxis=0\\\" fontsize=10];\\n  einsum1800417282080_ba_max_dim -> Concat12;\\n  einsum1800417282080_ba_left_dim -> Concat12;\\n  einsum1800417282080_ba_right_dim -> Concat12;\\n  Concat12 -> einsum1800417282080_ba_new_shape;\\n\\n  einsum1800417282080_ba_final [shape=box label=\\\"einsum1800417282080_ba_final\\\" fontsize=10];\\n  Reshape12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape12)\\\" fontsize=10];\\n  einsum1800417282080_ba_dot -> Reshape12;\\n  einsum1800417282080_ba_new_shape -> Reshape12;\\n  Reshape12 -> einsum1800417282080_ba_final;\\n\\n  einsum1800417282992_tr [shape=box label=\\\"einsum1800417282992_tr\\\" fontsize=10];\\n  Transpose1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1234)\\nperm=[0 3 4 1 2 5]\\\" fontsize=10];\\n  einsum1800417282080_ba_final -> Transpose1234;\\n  Transpose1234 -> einsum1800417282992_tr;\\n\\n  einsum1800417281840_id [shape=box label=\\\"einsum1800417281840_id\\\" fontsize=10];\\n  Identity12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity12)\\\" fontsize=10];\\n  X3 -> Identity12;\\n  Identity12 -> einsum1800417281840_id;\\n\\n  einsum1800417281936_ex [shape=box label=\\\"einsum1800417281936_ex\\\" fontsize=10];\\n  Unsqueeze12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze12)\\\" fontsize=10];\\n  einsum1800417281840_id -> Unsqueeze12;\\n  einsum1800417281840_id_axes -> Unsqueeze12;\\n  Unsqueeze12 -> einsum1800417281936_ex;\\n\\n  einsum1800417281888_re [shape=box label=\\\"einsum1800417281888_re\\\" fontsize=10];\\n  ReduceSum1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceSum\\n(ReduceSum1)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800417281936_ex -> ReduceSum1;\\n  einsum1800417281888_re_axes -> ReduceSum1;\\n  ReduceSum1 -> einsum1800417281888_re;\\n\\n  einsum1800417281696_tr [shape=box label=\\\"einsum1800417281696_tr\\\" fontsize=10];\\n  Transpose12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose12345)\\nperm=[0 5 1 2 4 3]\\\" fontsize=10];\\n  einsum1800417281888_re -> Transpose12345;\\n  Transpose12345 -> einsum1800417281696_tr;\\n\\n  einsum1800417282704_ba_shape1 [shape=box label=\\\"einsum1800417282704_ba_shape1\\\" fontsize=10];\\n  Shape12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape12)\\\" fontsize=10];\\n  einsum1800417282992_tr -> Shape12;\\n  Shape12 -> einsum1800417282704_ba_shape1;\\n\\n  einsum1800417282704_ba_shape2 [shape=box label=\\\"einsum1800417282704_ba_shape2\\\" fontsize=10];\\n  Shape123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape123)\\\" fontsize=10];\\n  einsum1800417281696_tr -> Shape123;\\n  Shape123 -> einsum1800417282704_ba_shape2;\\n\\n  einsum1800417282704_ba_dim0g [shape=box label=\\\"einsum1800417282704_ba_dim0g\\\" fontsize=10];\\n  Gather1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1234)\\\" fontsize=10];\\n  einsum1800417282704_ba_shape1 -> Gather1234;\\n  einsum1800417282704_ba_batch_axes -> Gather1234;\\n  Gather1234 -> einsum1800417282704_ba_dim0g;\\n\\n  einsum1800417282704_ba_dim0bg [shape=box label=\\\"einsum1800417282704_ba_dim0bg\\\" fontsize=10];\\n  Gather12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12345)\\\" fontsize=10];\\n  einsum1800417282704_ba_shape2 -> Gather12345;\\n  einsum1800417282704_ba_batch_axes -> Gather12345;\\n  Gather12345 -> einsum1800417282704_ba_dim0bg;\\n\\n  einsum1800417282704_ba_dim0 [shape=box label=\\\"einsum1800417282704_ba_dim0\\\" fontsize=10];\\n  ReduceProd12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd12)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800417282704_ba_dim0g -> ReduceProd12;\\n  ReduceProd12 -> einsum1800417282704_ba_dim0;\\n\\n  einsum1800417282704_ba_dim0b [shape=box label=\\\"einsum1800417282704_ba_dim0b\\\" fontsize=10];\\n  ReduceProd123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd123)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800417282704_ba_dim0bg -> ReduceProd123;\\n  ReduceProd123 -> einsum1800417282704_ba_dim0b;\\n\\n  einsum1800417282704_ba_dim1 [shape=box label=\\\"einsum1800417282704_ba_dim1\\\" fontsize=10];\\n  Gather123456 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123456)\\\" fontsize=10];\\n  einsum1800417282704_ba_shape1 -> Gather123456;\\n  einsum1800417282704_ba_sum_axes -> Gather123456;\\n  Gather123456 -> einsum1800417282704_ba_dim1;\\n\\n  einsum1800417282704_ba_dim2 [shape=box label=\\\"einsum1800417282704_ba_dim2\\\" fontsize=10];\\n  Gather1234567 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1234567)\\\" fontsize=10];\\n  einsum1800417282704_ba_shape2 -> Gather1234567;\\n  einsum1800417282704_ba_sum_axes -> Gather1234567;\\n  Gather1234567 -> einsum1800417282704_ba_dim2;\\n\\n  einsum1800417282704_ba_resh1 [shape=box label=\\\"einsum1800417282704_ba_resh1\\\" fontsize=10];\\n  Concat123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat123)\\naxis=0\\\" fontsize=10];\\n  einsum1800417282704_ba_dim0 -> Concat123;\\n  einsum1800417282704_ba__1 -> Concat123;\\n  einsum1800417282704_ba_dim1 -> Concat123;\\n  Concat123 -> einsum1800417282704_ba_resh1;\\n\\n  einsum1800417282704_ba_resh2 [shape=box label=\\\"einsum1800417282704_ba_resh2\\\" fontsize=10];\\n  Concat1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat1234)\\naxis=0\\\" fontsize=10];\\n  einsum1800417282704_ba_dim0b -> Concat1234;\\n  einsum1800417282704_ba__1 -> Concat1234;\\n  einsum1800417282704_ba_dim2 -> Concat1234;\\n  Concat1234 -> einsum1800417282704_ba_resh2;\\n\\n  einsum1800417282704_ba_aresh1 [shape=box label=\\\"einsum1800417282704_ba_aresh1\\\" fontsize=10];\\n  Reshape123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape123)\\\" fontsize=10];\\n  einsum1800417282992_tr -> Reshape123;\\n  einsum1800417282704_ba_resh1 -> Reshape123;\\n  Reshape123 -> einsum1800417282704_ba_aresh1;\\n\\n  einsum1800417282704_ba_aresh2 [shape=box label=\\\"einsum1800417282704_ba_aresh2\\\" fontsize=10];\\n  Reshape1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape1234)\\\" fontsize=10];\\n  einsum1800417281696_tr -> Reshape1234;\\n  einsum1800417282704_ba_resh2 -> Reshape1234;\\n  Reshape1234 -> einsum1800417282704_ba_aresh2;\\n\\n  einsum1800417282704_ba_aresh2_tr [shape=box label=\\\"einsum1800417282704_ba_aresh2_tr\\\" fontsize=10];\\n  Transpose123456 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose123456)\\nperm=[0 2 1]\\\" fontsize=10];\\n  einsum1800417282704_ba_aresh2 -> Transpose123456;\\n  Transpose123456 -> einsum1800417282704_ba_aresh2_tr;\\n\\n  einsum1800417282704_ba_dot [shape=box label=\\\"einsum1800417282704_ba_dot\\\" fontsize=10];\\n  MatMul1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"MatMul\\n(MatMul1)\\\" fontsize=10];\\n  einsum1800417282704_ba_aresh1 -> MatMul1;\\n  einsum1800417282704_ba_aresh2_tr -> MatMul1;\\n  MatMul1 -> einsum1800417282704_ba_dot;\\n\\n  einsum1800417282704_ba_max_dim [shape=box label=\\\"einsum1800417282704_ba_max_dim\\\" fontsize=10];\\n  Max1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Max\\n(Max1)\\\" fontsize=10];\\n  einsum1800417282704_ba_dim0g -> Max1;\\n  einsum1800417282704_ba_dim0bg -> Max1;\\n  Max1 -> einsum1800417282704_ba_max_dim;\\n\\n  einsum1800417282704_ba_left_dim [shape=box label=\\\"einsum1800417282704_ba_left_dim\\\" fontsize=10];\\n  Gather12345678 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12345678)\\\" fontsize=10];\\n  einsum1800417282704_ba_shape1 -> Gather12345678;\\n  einsum1800417282704_ba_left_set -> Gather12345678;\\n  Gather12345678 -> einsum1800417282704_ba_left_dim;\\n\\n  einsum1800417282704_ba_right_dim [shape=box label=\\\"einsum1800417282704_ba_right_dim\\\" fontsize=10];\\n  Gather123456789 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123456789)\\\" fontsize=10];\\n  einsum1800417282704_ba_shape2 -> Gather123456789;\\n  einsum1800417282704_ba_right_set -> Gather123456789;\\n  Gather123456789 -> einsum1800417282704_ba_right_dim;\\n\\n  einsum1800417282704_ba_new_shape [shape=box label=\\\"einsum1800417282704_ba_new_shape\\\" fontsize=10];\\n  Concat12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat12345)\\naxis=0\\\" fontsize=10];\\n  einsum1800417282704_ba_max_dim -> Concat12345;\\n  einsum1800417282704_ba_left_dim -> Concat12345;\\n  einsum1800417282704_ba_right_dim -> Concat12345;\\n  einsum1800417282704_ba_ones -> Concat12345;\\n  Concat12345 -> einsum1800417282704_ba_new_shape;\\n\\n  einsum1800417282704_ba_final [shape=box label=\\\"einsum1800417282704_ba_final\\\" fontsize=10];\\n  Reshape12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape12345)\\\" fontsize=10];\\n  einsum1800417282704_ba_dot -> Reshape12345;\\n  einsum1800417282704_ba_new_shape -> Reshape12345;\\n  Reshape12345 -> einsum1800417282704_ba_final;\\n\\n  einsum1800417282416_tr [shape=box label=\\\"einsum1800417282416_tr\\\" fontsize=10];\\n  Transpose1234567 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1234567)\\nperm=[0 4 2 5 3 1]\\\" fontsize=10];\\n  einsum1800417282704_ba_final -> Transpose1234567;\\n  Transpose1234567 -> einsum1800417282416_tr;\\n\\n  einsum1800417282512_sq [shape=box label=\\\"einsum1800417282512_sq\\\" fontsize=10];\\n  Squeeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Squeeze\\n(Squeeze)\\\" fontsize=10];\\n  einsum1800417282416_tr -> Squeeze;\\n  einsum1800417282416_tr_axes -> Squeeze;\\n  Squeeze -> einsum1800417282512_sq;\\n\\n  Identity123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity123)\\\" fontsize=10];\\n  einsum1800417282512_sq -> Identity123;\\n  Identity123 -> Y;\\n}\");\n",
-              "document.getElementById('M2f6ab9de93dc4a15b1617de91aeb3e83').innerHTML = svgGraph; });\n",
+              "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\n  orientation=portrait;\\n  size=None;\\n  ranksep=0.25;\\n  nodesep=0.05;\\n\\n  X1 [shape=box color=red label=\\\"X1\\nfloat((0, 0, 0))\\\" fontsize=10];\\n  X2 [shape=box color=red label=\\\"X2\\nfloat((0, 0))\\\" fontsize=10];\\n  X3 [shape=box color=red label=\\\"X3\\nfloat((0, 0, 0))\\\" fontsize=10];\\n\\n  Y [shape=box color=green label=\\\"Y\\nfloat((0, 0, 0))\\\" fontsize=10];\\n\\n  einsum2447533426480_id_axes [shape=box label=\\\"einsum2447533426480_id_axes\\nint64((3,))\\n[3 4 5]\\\" fontsize=10];\\n  einsum2447546940096_re_axes [shape=box label=\\\"einsum2447546940096_re_axes\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum2447546939712_id_axes [shape=box label=\\\"einsum2447546939712_id_axes\\nint64((4,))\\n[0 1 4 5]\\\" fontsize=10];\\n  einsum2447546939760_ba_1 [shape=box label=\\\"einsum2447546939760_ba_1\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum2447546939760_ba_0 [shape=box label=\\\"einsum2447546939760_ba_0\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum2447546939760_ba_batch_axes [shape=box label=\\\"einsum2447546939760_ba_batch_axes\\nint64((4,))\\n[0 1 2 3]\\\" fontsize=10];\\n  einsum2447546939760_ba__1 [shape=box label=\\\"einsum2447546939760_ba__1\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum2447546939760_ba_left_set [shape=box label=\\\"einsum2447546939760_ba_left_set\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum2447546939760_ba_right_set [shape=box label=\\\"einsum2447546939760_ba_right_set\\nint64((1,))\\n[5]\\\" fontsize=10];\\n  einsum2447546941104_id_axes [shape=box label=\\\"einsum2447546941104_id_axes\\nint64((3,))\\n[0 1 2]\\\" fontsize=10];\\n  einsum2447546939280_re_axes [shape=box label=\\\"einsum2447546939280_re_axes\\nint64((1,))\\n[5]\\\" fontsize=10];\\n  einsum2447550472688_ba_1 [shape=box label=\\\"einsum2447550472688_ba_1\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum2447550472688_ba_0 [shape=box label=\\\"einsum2447550472688_ba_0\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum2447550472688_ba_batch_axes [shape=box label=\\\"einsum2447550472688_ba_batch_axes\\nint64((2,))\\n[0 1]\\\" fontsize=10];\\n  einsum2447550472688_ba_sum_axes [shape=box label=\\\"einsum2447550472688_ba_sum_axes\\nint64((1,))\\n[5]\\\" fontsize=10];\\n  einsum2447550472688_ba__1 [shape=box label=\\\"einsum2447550472688_ba__1\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum2447550472688_ba__01 [shape=box label=\\\"einsum2447550472688_ba__01\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum2447550472688_ba_left_set [shape=box label=\\\"einsum2447550472688_ba_left_set\\nint64((2,))\\n[2 3]\\\" fontsize=10];\\n  einsum2447550472688_ba_right_set [shape=box label=\\\"einsum2447550472688_ba_right_set\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum2447550472688_ba_ones [shape=box label=\\\"einsum2447550472688_ba_ones\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum2447546940576_tr_axes [shape=box label=\\\"einsum2447546940576_tr_axes\\nint64((3,))\\n[0 3 5]\\\" fontsize=10];\\n\\n  einsum2447533426480_id [shape=box label=\\\"einsum2447533426480_id\\\" fontsize=10];\\n  Identity [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity)\\\" fontsize=10];\\n  X1 -> Identity;\\n  Identity -> einsum2447533426480_id;\\n\\n  einsum2446817491792_ex [shape=box label=\\\"einsum2446817491792_ex\\\" fontsize=10];\\n  Unsqueeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze)\\\" fontsize=10];\\n  einsum2447533426480_id -> Unsqueeze;\\n  einsum2447533426480_id_axes -> Unsqueeze;\\n  Unsqueeze -> einsum2446817491792_ex;\\n\\n  einsum2447546939328_tr [shape=box label=\\\"einsum2447546939328_tr\\\" fontsize=10];\\n  Transpose [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose)\\nperm=[1 0 2 3 4 5]\\\" fontsize=10];\\n  einsum2446817491792_ex -> Transpose;\\n  Transpose -> einsum2447546939328_tr;\\n\\n  einsum2447546940096_re [shape=box label=\\\"einsum2447546940096_re\\\" fontsize=10];\\n  ReduceSum [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceSum\\n(ReduceSum)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447546939328_tr -> ReduceSum;\\n  einsum2447546940096_re_axes -> ReduceSum;\\n  ReduceSum -> einsum2447546940096_re;\\n\\n  einsum2447546939712_id [shape=box label=\\\"einsum2447546939712_id\\\" fontsize=10];\\n  Identity1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity1)\\\" fontsize=10];\\n  X2 -> Identity1;\\n  Identity1 -> einsum2447546939712_id;\\n\\n  einsum2447546939616_ex [shape=box label=\\\"einsum2447546939616_ex\\\" fontsize=10];\\n  Unsqueeze1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze1)\\\" fontsize=10];\\n  einsum2447546939712_id -> Unsqueeze1;\\n  einsum2447546939712_id_axes -> Unsqueeze1;\\n  Unsqueeze1 -> einsum2447546939616_ex;\\n\\n  einsum2447546939856_tr [shape=box label=\\\"einsum2447546939856_tr\\\" fontsize=10];\\n  Transpose1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1)\\nperm=[0 2 4 5 1 3]\\\" fontsize=10];\\n  einsum2447546940096_re -> Transpose1;\\n  Transpose1 -> einsum2447546939856_tr;\\n\\n  einsum2447546940624_tr [shape=box label=\\\"einsum2447546940624_tr\\\" fontsize=10];\\n  Transpose12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose12)\\nperm=[0 2 4 5 1 3]\\\" fontsize=10];\\n  einsum2447546939616_ex -> Transpose12;\\n  Transpose12 -> einsum2447546940624_tr;\\n\\n  einsum2447546939760_ba_shape1 [shape=box label=\\\"einsum2447546939760_ba_shape1\\\" fontsize=10];\\n  Shape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape)\\\" fontsize=10];\\n  einsum2447546939856_tr -> Shape;\\n  Shape -> einsum2447546939760_ba_shape1;\\n\\n  einsum2447546939760_ba_shape2 [shape=box label=\\\"einsum2447546939760_ba_shape2\\\" fontsize=10];\\n  Shape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape1)\\\" fontsize=10];\\n  einsum2447546940624_tr -> Shape1;\\n  Shape1 -> einsum2447546939760_ba_shape2;\\n\\n  einsum2447546939760_ba_dim0g [shape=box label=\\\"einsum2447546939760_ba_dim0g\\\" fontsize=10];\\n  Gather [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather)\\\" fontsize=10];\\n  einsum2447546939760_ba_shape1 -> Gather;\\n  einsum2447546939760_ba_batch_axes -> Gather;\\n  Gather -> einsum2447546939760_ba_dim0g;\\n\\n  einsum2447546939760_ba_dim0bg [shape=box label=\\\"einsum2447546939760_ba_dim0bg\\\" fontsize=10];\\n  Gather1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1)\\\" fontsize=10];\\n  einsum2447546939760_ba_shape2 -> Gather1;\\n  einsum2447546939760_ba_batch_axes -> Gather1;\\n  Gather1 -> einsum2447546939760_ba_dim0bg;\\n\\n  einsum2447546939760_ba_dim0 [shape=box label=\\\"einsum2447546939760_ba_dim0\\\" fontsize=10];\\n  ReduceProd [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447546939760_ba_dim0g -> ReduceProd;\\n  ReduceProd -> einsum2447546939760_ba_dim0;\\n\\n  einsum2447546939760_ba_dim0b [shape=box label=\\\"einsum2447546939760_ba_dim0b\\\" fontsize=10];\\n  ReduceProd1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd1)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447546939760_ba_dim0bg -> ReduceProd1;\\n  ReduceProd1 -> einsum2447546939760_ba_dim0b;\\n\\n  einsum2447546939760_ba_resh1 [shape=box label=\\\"einsum2447546939760_ba_resh1\\\" fontsize=10];\\n  Concat [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat)\\naxis=0\\\" fontsize=10];\\n  einsum2447546939760_ba_dim0 -> Concat;\\n  einsum2447546939760_ba__1 -> Concat;\\n  einsum2447546939760_ba_1 -> Concat;\\n  Concat -> einsum2447546939760_ba_resh1;\\n\\n  einsum2447546939760_ba_resh2 [shape=box label=\\\"einsum2447546939760_ba_resh2\\\" fontsize=10];\\n  Concat1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat1)\\naxis=0\\\" fontsize=10];\\n  einsum2447546939760_ba_dim0b -> Concat1;\\n  einsum2447546939760_ba__1 -> Concat1;\\n  einsum2447546939760_ba_1 -> Concat1;\\n  Concat1 -> einsum2447546939760_ba_resh2;\\n\\n  einsum2447546939760_ba_aresh1 [shape=box label=\\\"einsum2447546939760_ba_aresh1\\\" fontsize=10];\\n  Reshape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape)\\\" fontsize=10];\\n  einsum2447546939856_tr -> Reshape;\\n  einsum2447546939760_ba_resh1 -> Reshape;\\n  Reshape -> einsum2447546939760_ba_aresh1;\\n\\n  einsum2447546939760_ba_aresh2 [shape=box label=\\\"einsum2447546939760_ba_aresh2\\\" fontsize=10];\\n  Reshape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape1)\\\" fontsize=10];\\n  einsum2447546940624_tr -> Reshape1;\\n  einsum2447546939760_ba_resh2 -> Reshape1;\\n  Reshape1 -> einsum2447546939760_ba_aresh2;\\n\\n  einsum2447546939760_ba_aresh2_tr [shape=box label=\\\"einsum2447546939760_ba_aresh2_tr\\\" fontsize=10];\\n  Transpose123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose123)\\nperm=[0 2 1]\\\" fontsize=10];\\n  einsum2447546939760_ba_aresh2 -> Transpose123;\\n  Transpose123 -> einsum2447546939760_ba_aresh2_tr;\\n\\n  einsum2447546939760_ba_dot [shape=box label=\\\"einsum2447546939760_ba_dot\\\" fontsize=10];\\n  MatMul [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"MatMul\\n(MatMul)\\\" fontsize=10];\\n  einsum2447546939760_ba_aresh1 -> MatMul;\\n  einsum2447546939760_ba_aresh2_tr -> MatMul;\\n  MatMul -> einsum2447546939760_ba_dot;\\n\\n  einsum2447546939760_ba_max_dim [shape=box label=\\\"einsum2447546939760_ba_max_dim\\\" fontsize=10];\\n  Max [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Max\\n(Max)\\\" fontsize=10];\\n  einsum2447546939760_ba_dim0g -> Max;\\n  einsum2447546939760_ba_dim0bg -> Max;\\n  Max -> einsum2447546939760_ba_max_dim;\\n\\n  einsum2447546939760_ba_left_dim [shape=box label=\\\"einsum2447546939760_ba_left_dim\\\" fontsize=10];\\n  Gather12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12)\\\" fontsize=10];\\n  einsum2447546939760_ba_shape1 -> Gather12;\\n  einsum2447546939760_ba_left_set -> Gather12;\\n  Gather12 -> einsum2447546939760_ba_left_dim;\\n\\n  einsum2447546939760_ba_right_dim [shape=box label=\\\"einsum2447546939760_ba_right_dim\\\" fontsize=10];\\n  Gather123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123)\\\" fontsize=10];\\n  einsum2447546939760_ba_shape2 -> Gather123;\\n  einsum2447546939760_ba_right_set -> Gather123;\\n  Gather123 -> einsum2447546939760_ba_right_dim;\\n\\n  einsum2447546939760_ba_new_shape [shape=box label=\\\"einsum2447546939760_ba_new_shape\\\" fontsize=10];\\n  Concat12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat12)\\naxis=0\\\" fontsize=10];\\n  einsum2447546939760_ba_max_dim -> Concat12;\\n  einsum2447546939760_ba_left_dim -> Concat12;\\n  einsum2447546939760_ba_right_dim -> Concat12;\\n  Concat12 -> einsum2447546939760_ba_new_shape;\\n\\n  einsum2447546939760_ba_final [shape=box label=\\\"einsum2447546939760_ba_final\\\" fontsize=10];\\n  Reshape12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape12)\\\" fontsize=10];\\n  einsum2447546939760_ba_dot -> Reshape12;\\n  einsum2447546939760_ba_new_shape -> Reshape12;\\n  Reshape12 -> einsum2447546939760_ba_final;\\n\\n  einsum2447550472832_tr [shape=box label=\\\"einsum2447550472832_tr\\\" fontsize=10];\\n  Transpose1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1234)\\nperm=[0 3 4 1 2 5]\\\" fontsize=10];\\n  einsum2447546939760_ba_final -> Transpose1234;\\n  Transpose1234 -> einsum2447550472832_tr;\\n\\n  einsum2447546941104_id [shape=box label=\\\"einsum2447546941104_id\\\" fontsize=10];\\n  Identity12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity12)\\\" fontsize=10];\\n  X3 -> Identity12;\\n  Identity12 -> einsum2447546941104_id;\\n\\n  einsum2447546940432_ex [shape=box label=\\\"einsum2447546940432_ex\\\" fontsize=10];\\n  Unsqueeze12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze12)\\\" fontsize=10];\\n  einsum2447546941104_id -> Unsqueeze12;\\n  einsum2447546941104_id_axes -> Unsqueeze12;\\n  Unsqueeze12 -> einsum2447546940432_ex;\\n\\n  einsum2447546939280_re [shape=box label=\\\"einsum2447546939280_re\\\" fontsize=10];\\n  ReduceSum1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceSum\\n(ReduceSum1)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447546940432_ex -> ReduceSum1;\\n  einsum2447546939280_re_axes -> ReduceSum1;\\n  ReduceSum1 -> einsum2447546939280_re;\\n\\n  einsum2447550473216_tr [shape=box label=\\\"einsum2447550473216_tr\\\" fontsize=10];\\n  Transpose12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose12345)\\nperm=[0 5 1 2 4 3]\\\" fontsize=10];\\n  einsum2447546939280_re -> Transpose12345;\\n  Transpose12345 -> einsum2447550473216_tr;\\n\\n  einsum2447550472688_ba_shape1 [shape=box label=\\\"einsum2447550472688_ba_shape1\\\" fontsize=10];\\n  Shape12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape12)\\\" fontsize=10];\\n  einsum2447550472832_tr -> Shape12;\\n  Shape12 -> einsum2447550472688_ba_shape1;\\n\\n  einsum2447550472688_ba_shape2 [shape=box label=\\\"einsum2447550472688_ba_shape2\\\" fontsize=10];\\n  Shape123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape123)\\\" fontsize=10];\\n  einsum2447550473216_tr -> Shape123;\\n  Shape123 -> einsum2447550472688_ba_shape2;\\n\\n  einsum2447550472688_ba_dim0g [shape=box label=\\\"einsum2447550472688_ba_dim0g\\\" fontsize=10];\\n  Gather1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1234)\\\" fontsize=10];\\n  einsum2447550472688_ba_shape1 -> Gather1234;\\n  einsum2447550472688_ba_batch_axes -> Gather1234;\\n  Gather1234 -> einsum2447550472688_ba_dim0g;\\n\\n  einsum2447550472688_ba_dim0bg [shape=box label=\\\"einsum2447550472688_ba_dim0bg\\\" fontsize=10];\\n  Gather12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12345)\\\" fontsize=10];\\n  einsum2447550472688_ba_shape2 -> Gather12345;\\n  einsum2447550472688_ba_batch_axes -> Gather12345;\\n  Gather12345 -> einsum2447550472688_ba_dim0bg;\\n\\n  einsum2447550472688_ba_dim0 [shape=box label=\\\"einsum2447550472688_ba_dim0\\\" fontsize=10];\\n  ReduceProd12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd12)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447550472688_ba_dim0g -> ReduceProd12;\\n  ReduceProd12 -> einsum2447550472688_ba_dim0;\\n\\n  einsum2447550472688_ba_dim0b [shape=box label=\\\"einsum2447550472688_ba_dim0b\\\" fontsize=10];\\n  ReduceProd123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd123)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447550472688_ba_dim0bg -> ReduceProd123;\\n  ReduceProd123 -> einsum2447550472688_ba_dim0b;\\n\\n  einsum2447550472688_ba_dim1 [shape=box label=\\\"einsum2447550472688_ba_dim1\\\" fontsize=10];\\n  Gather123456 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123456)\\\" fontsize=10];\\n  einsum2447550472688_ba_shape1 -> Gather123456;\\n  einsum2447550472688_ba_sum_axes -> Gather123456;\\n  Gather123456 -> einsum2447550472688_ba_dim1;\\n\\n  einsum2447550472688_ba_dim2 [shape=box label=\\\"einsum2447550472688_ba_dim2\\\" fontsize=10];\\n  Gather1234567 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1234567)\\\" fontsize=10];\\n  einsum2447550472688_ba_shape2 -> Gather1234567;\\n  einsum2447550472688_ba_sum_axes -> Gather1234567;\\n  Gather1234567 -> einsum2447550472688_ba_dim2;\\n\\n  einsum2447550472688_ba_resh1_11 [shape=box label=\\\"einsum2447550472688_ba_resh1_11\\\" fontsize=10];\\n  Concat123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat123)\\naxis=0\\\" fontsize=10];\\n  einsum2447550472688_ba__01 -> Concat123;\\n  einsum2447550472688_ba_dim1 -> Concat123;\\n  Concat123 -> einsum2447550472688_ba_resh1_11;\\n\\n  einsum2447550472688_ba_resh2_11 [shape=box label=\\\"einsum2447550472688_ba_resh2_11\\\" fontsize=10];\\n  Concat1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat1234)\\naxis=0\\\" fontsize=10];\\n  einsum2447550472688_ba__01 -> Concat1234;\\n  einsum2447550472688_ba_dim2 -> Concat1234;\\n  Concat1234 -> einsum2447550472688_ba_resh2_11;\\n\\n  einsum2447550472688_ba_aresh1 [shape=box label=\\\"einsum2447550472688_ba_aresh1\\\" fontsize=10];\\n  Reshape123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape123)\\\" fontsize=10];\\n  einsum2447550472832_tr -> Reshape123;\\n  einsum2447550472688_ba_resh1_11 -> Reshape123;\\n  Reshape123 -> einsum2447550472688_ba_aresh1;\\n\\n  einsum2447550472688_ba_aresh2 [shape=box label=\\\"einsum2447550472688_ba_aresh2\\\" fontsize=10];\\n  Reshape1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape1234)\\\" fontsize=10];\\n  einsum2447550473216_tr -> Reshape1234;\\n  einsum2447550472688_ba_resh2_11 -> Reshape1234;\\n  Reshape1234 -> einsum2447550472688_ba_aresh2;\\n\\n  einsum2447550472688_ba_gemm [shape=box label=\\\"einsum2447550472688_ba_gemm\\\" fontsize=10];\\n  Gemm [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gemm\\n(Gemm)\\nalpha=1.0\\nbeta=0.0\\ntransA=0\\ntransB=1\\\" fontsize=10];\\n  einsum2447550472688_ba_aresh1 -> Gemm;\\n  einsum2447550472688_ba_aresh2 -> Gemm;\\n  Gemm -> einsum2447550472688_ba_gemm;\\n\\n  einsum2447550472688_ba_max_dim [shape=box label=\\\"einsum2447550472688_ba_max_dim\\\" fontsize=10];\\n  Max1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Max\\n(Max1)\\\" fontsize=10];\\n  einsum2447550472688_ba_dim0g -> Max1;\\n  einsum2447550472688_ba_dim0bg -> Max1;\\n  Max1 -> einsum2447550472688_ba_max_dim;\\n\\n  einsum2447550472688_ba_left_dim [shape=box label=\\\"einsum2447550472688_ba_left_dim\\\" fontsize=10];\\n  Gather12345678 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12345678)\\\" fontsize=10];\\n  einsum2447550472688_ba_shape1 -> Gather12345678;\\n  einsum2447550472688_ba_left_set -> Gather12345678;\\n  Gather12345678 -> einsum2447550472688_ba_left_dim;\\n\\n  einsum2447550472688_ba_right_dim [shape=box label=\\\"einsum2447550472688_ba_right_dim\\\" fontsize=10];\\n  Gather123456789 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123456789)\\\" fontsize=10];\\n  einsum2447550472688_ba_shape2 -> Gather123456789;\\n  einsum2447550472688_ba_right_set -> Gather123456789;\\n  Gather123456789 -> einsum2447550472688_ba_right_dim;\\n\\n  einsum2447550472688_ba_new_shape [shape=box label=\\\"einsum2447550472688_ba_new_shape\\\" fontsize=10];\\n  Concat12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat12345)\\naxis=0\\\" fontsize=10];\\n  einsum2447550472688_ba_max_dim -> Concat12345;\\n  einsum2447550472688_ba_left_dim -> Concat12345;\\n  einsum2447550472688_ba_right_dim -> Concat12345;\\n  einsum2447550472688_ba_ones -> Concat12345;\\n  Concat12345 -> einsum2447550472688_ba_new_shape;\\n\\n  einsum2447550472688_ba_final [shape=box label=\\\"einsum2447550472688_ba_final\\\" fontsize=10];\\n  Reshape12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape12345)\\\" fontsize=10];\\n  einsum2447550472688_ba_gemm -> Reshape12345;\\n  einsum2447550472688_ba_new_shape -> Reshape12345;\\n  Reshape12345 -> einsum2447550472688_ba_final;\\n\\n  einsum2447546940576_tr [shape=box label=\\\"einsum2447546940576_tr\\\" fontsize=10];\\n  Transpose123456 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose123456)\\nperm=[0 4 2 5 3 1]\\\" fontsize=10];\\n  einsum2447550472688_ba_final -> Transpose123456;\\n  Transpose123456 -> einsum2447546940576_tr;\\n\\n  einsum2446817537664_sq [shape=box label=\\\"einsum2446817537664_sq\\\" fontsize=10];\\n  Squeeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Squeeze\\n(Squeeze)\\\" fontsize=10];\\n  einsum2447546940576_tr -> Squeeze;\\n  einsum2447546940576_tr_axes -> Squeeze;\\n  Squeeze -> einsum2446817537664_sq;\\n\\n  Identity123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity123)\\\" fontsize=10];\\n  einsum2446817537664_sq -> Identity123;\\n  Identity123 -> Y;\\n}\");\n",
+              "document.getElementById('M2677f08d77ee4a638da84ed324602269').innerHTML = svgGraph; });\n",
               "\n",
               "</script>"
             ],
             "text/plain": [
-              "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x1a3313e42b0>"
+              "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x239dd3462e0>"
             ]
           },
           "execution_count": 12,
@@ -547,7 +547,7 @@
           "text": [
             "C:\\xavierdupre\\__home_\\github_fork\\scikit-learn\\sklearn\\experimental\\enable_hist_gradient_boosting.py:16: UserWarning: Since version 1.0, it is not needed to import enable_hist_gradient_boosting anymore. HistGradientBoostingClassifier and HistGradientBoostingRegressor are now stable and can be normally imported from sklearn.ensemble.\n",
             "  warnings.warn(\n",
-            "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14/14 [00:19<00:00,  1.38s/it]\n"
+            "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 14/14 [00:22<00:00,  1.58s/it]\n"
           ]
         },
         {
@@ -585,61 +585,61 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>82</th>\n",
-              "      <td>0.062646</td>\n",
-              "      <td>0.000681</td>\n",
-              "      <td>0.062002</td>\n",
-              "      <td>0.063933</td>\n",
+              "      <td>0.065264</td>\n",
+              "      <td>0.001133</td>\n",
+              "      <td>0.064566</td>\n",
+              "      <td>0.068613</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.626458</td>\n",
+              "      <td>0.652640</td>\n",
               "      <td>custom_einsum</td>\n",
               "      <td>60</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>83</th>\n",
-              "      <td>0.048764</td>\n",
-              "      <td>0.001461</td>\n",
-              "      <td>0.047808</td>\n",
-              "      <td>0.052906</td>\n",
+              "      <td>0.051281</td>\n",
+              "      <td>0.001298</td>\n",
+              "      <td>0.049810</td>\n",
+              "      <td>0.053852</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.487644</td>\n",
+              "      <td>0.512811</td>\n",
               "      <td>dec-matmul</td>\n",
               "      <td>60</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>84</th>\n",
-              "      <td>0.040966</td>\n",
-              "      <td>0.000602</td>\n",
-              "      <td>0.040169</td>\n",
-              "      <td>0.041773</td>\n",
+              "      <td>0.068202</td>\n",
+              "      <td>0.010795</td>\n",
+              "      <td>0.058989</td>\n",
+              "      <td>0.090383</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.409658</td>\n",
+              "      <td>0.682015</td>\n",
               "      <td>dec-batch_dot</td>\n",
               "      <td>60</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>85</th>\n",
-              "      <td>0.010761</td>\n",
-              "      <td>0.000982</td>\n",
-              "      <td>0.009832</td>\n",
-              "      <td>0.012314</td>\n",
+              "      <td>0.016590</td>\n",
+              "      <td>0.001776</td>\n",
+              "      <td>0.013965</td>\n",
+              "      <td>0.021065</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.107609</td>\n",
+              "      <td>0.165899</td>\n",
               "      <td>ort-einsum</td>\n",
               "      <td>60</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>86</th>\n",
-              "      <td>0.015497</td>\n",
-              "      <td>0.000365</td>\n",
-              "      <td>0.014887</td>\n",
-              "      <td>0.015853</td>\n",
+              "      <td>0.018336</td>\n",
+              "      <td>0.002004</td>\n",
+              "      <td>0.016014</td>\n",
+              "      <td>0.021313</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.154967</td>\n",
+              "      <td>0.183363</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>60</td>\n",
               "    </tr>\n",
@@ -649,11 +649,11 @@
             ],
             "text/plain": [
               "     average  deviation  min_exec  max_exec  repeat  number     total  \\\n",
-              "82  0.062646   0.000681  0.062002  0.063933      10      10  0.626458   \n",
-              "83  0.048764   0.001461  0.047808  0.052906      10      10  0.487644   \n",
-              "84  0.040966   0.000602  0.040169  0.041773      10      10  0.409658   \n",
-              "85  0.010761   0.000982  0.009832  0.012314      10      10  0.107609   \n",
-              "86  0.015497   0.000365  0.014887  0.015853      10      10  0.154967   \n",
+              "82  0.065264   0.001133  0.064566  0.068613      10      10  0.652640   \n",
+              "83  0.051281   0.001298  0.049810  0.053852      10      10  0.512811   \n",
+              "84  0.068202   0.010795  0.058989  0.090383      10      10  0.682015   \n",
+              "85  0.016590   0.001776  0.013965  0.021065      10      10  0.165899   \n",
+              "86  0.018336   0.002004  0.016014  0.021313      10      10  0.183363   \n",
               "\n",
               "             name   N  \n",
               "82  custom_einsum  60  \n",
@@ -776,7 +776,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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\n",
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\n",
             "text/plain": [
               "<Figure size 1008x432 with 2 Axes>"
             ]
@@ -834,16 +834,16 @@
         {
           "data": {
             "text/html": [
-              "<div id=\"Mbe8aaf61ea2947c9a2e778246000d1b9-cont\"><div id=\"Mbe8aaf61ea2947c9a2e778246000d1b9\" style=\"width:100%;height:100%;\"></div></div>\n",
+              "<div id=\"M378a6780ecd8481ba2539d2793816dc4-cont\"><div id=\"M378a6780ecd8481ba2539d2793816dc4\" style=\"width:100%;height:100%;\"></div></div>\n",
               "<script>\n",
               "\n",
-              "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\norientation=portrait;\\nranksep=0.25;\\nnodesep=0.05;\\nwidth=0.5;\\nheight=0.1;\\nsize=7;\\nnode [shape=record];\\n0 [label=\\\"input 0\\\\nbac\\\\n[ 1  0  2 -1 -1 -1]\\\"];\\n1800417238368 [label=\\\"id\\\\nNone\\\"];\\n0 -> 1800417238368;\\n1800417281600 [label=\\\"expand_dims\\\\naxes=((3, 3), (3, 4), (3, 5))None\\\"];\\n1800417238368 -> 1800417281600;\\n1800417281552 [label=\\\"transpose\\\\nperm=(1, 0, 2, 3, 4, 5)None\\\"];\\n1800417281600 -> 1800417281552;\\n1800417281120 [label=\\\"reduce_sum - I0\\\\naxes=(0,)None\\\" style=filled fillcolor=red];\\n1800417281552 -> 1800417281120;\\n1 [label=\\\"input 1\\\\ncd\\\\n[-1 -1  0  1 -1 -1]\\\"];\\n1800417281072 [label=\\\"id\\\\nNone\\\"];\\n1 -> 1800417281072;\\n1800417280352 [label=\\\"expand_dims\\\\naxes=((0, 0), (0, 1), (2, 4), (2, 5))None\\\"];\\n1800417281072 -> 1800417280352;\\n1800417281360 [label=\\\"transpose\\\\nperm=(0, 2, 4, 5, 1, 3)None\\\"];\\n1800417281120 -> 1800417281360;\\n1800417281216 [label=\\\"transpose\\\\nperm=(0, 2, 4, 5, 1, 3)None\\\"];\\n1800417280352 -> 1800417281216;\\n1800417282080 [label=\\\"batch_dot\\\\nbatch_axes=(0, 1, 2, 3) keep_axes=None left=(1, 4) ndim=6 right=(1, 5) sum_axes=()None\\\"];\\n1800417281360 -> 1800417282080;\\n1800417281216 -> 1800417282080;\\n2 [label=\\\"input 2\\\\ndef\\\\n[-1 -1 -1  0  1  2]\\\"];\\n1800417281840 [label=\\\"id\\\\nNone\\\"];\\n2 -> 1800417281840;\\n1800417281936 [label=\\\"expand_dims\\\\naxes=((0, 0), (0, 1), (0, 2))None\\\"];\\n1800417281840 -> 1800417281936;\\n1800417281888 [label=\\\"reduce_sum\\\\naxes=(5,)None\\\"];\\n1800417281936 -> 1800417281888;\\n1800417281696 [label=\\\"transpose\\\\nperm=(0, 5, 1, 2, 4, 3)None\\\"];\\n1800417281888 -> 1800417281696;\\n1800417282704 [label=\\\"batch_dot\\\\nbatch_axes=(0, 1) keep_axes=None left=(2, 3) ndim=6 right=(4,) sum_axes=(5,)None\\\"];\\n1800417282992 -> 1800417282704;\\n1800417281696 -> 1800417282704;\\n1800417282512 [label=\\\"squeeze - I-1\\\\naxes=(0, 3, 5)None\\\" style=filled fillcolor=red];\\n1800417282416 -> 1800417282512;\\n1800417282992 [label=\\\"transpose - I1\\\\nperm=(0, 3, 4, 1, 2, 5)None\\\" style=filled fillcolor=red];\\n1800417282080 -> 1800417282992;\\n1800417282416 [label=\\\"transpose - I2\\\\nperm=(0, 4, 2, 5, 3, 1)None\\\" style=filled fillcolor=red];\\n1800417282704 -> 1800417282416;\\n}\");\n",
-              "document.getElementById('Mbe8aaf61ea2947c9a2e778246000d1b9').innerHTML = svgGraph; });\n",
+              "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\norientation=portrait;\\nranksep=0.25;\\nnodesep=0.05;\\nwidth=0.5;\\nheight=0.1;\\nsize=7;\\nnode [shape=record];\\n0 [label=\\\"input 0\\\\nbac\\\\n[ 1  0  2 -1 -1 -1]\\\"];\\n2447533426480 [label=\\\"id\\\\nNone\\\"];\\n0 -> 2447533426480;\\n2446817491792 [label=\\\"expand_dims\\\\naxes=((3, 3), (3, 4), (3, 5))None\\\"];\\n2447533426480 -> 2446817491792;\\n2447546939328 [label=\\\"transpose\\\\nperm=(1, 0, 2, 3, 4, 5)None\\\"];\\n2446817491792 -> 2447546939328;\\n2447546940096 [label=\\\"reduce_sum - I0\\\\naxes=(0,)None\\\" style=filled fillcolor=red];\\n2447546939328 -> 2447546940096;\\n1 [label=\\\"input 1\\\\ncd\\\\n[-1 -1  0  1 -1 -1]\\\"];\\n2447546939712 [label=\\\"id\\\\nNone\\\"];\\n1 -> 2447546939712;\\n2447546939616 [label=\\\"expand_dims\\\\naxes=((0, 0), (0, 1), (2, 4), (2, 5))None\\\"];\\n2447546939712 -> 2447546939616;\\n2447546939856 [label=\\\"transpose\\\\nperm=(0, 2, 4, 5, 1, 3)None\\\"];\\n2447546940096 -> 2447546939856;\\n2447546940624 [label=\\\"transpose\\\\nperm=(0, 2, 4, 5, 1, 3)None\\\"];\\n2447546939616 -> 2447546940624;\\n2447546939760 [label=\\\"batch_dot\\\\nbatch_axes=(0, 1, 2, 3) keep_axes=None left=(1, 4) ndim=6 right=(1, 5) sum_axes=()None\\\"];\\n2447546939856 -> 2447546939760;\\n2447546940624 -> 2447546939760;\\n2 [label=\\\"input 2\\\\ndef\\\\n[-1 -1 -1  0  1  2]\\\"];\\n2447546941104 [label=\\\"id\\\\nNone\\\"];\\n2 -> 2447546941104;\\n2447546940432 [label=\\\"expand_dims\\\\naxes=((0, 0), (0, 1), (0, 2))None\\\"];\\n2447546941104 -> 2447546940432;\\n2447546939280 [label=\\\"reduce_sum\\\\naxes=(5,)None\\\"];\\n2447546940432 -> 2447546939280;\\n2447550473216 [label=\\\"transpose\\\\nperm=(0, 5, 1, 2, 4, 3)None\\\"];\\n2447546939280 -> 2447550473216;\\n2447550472688 [label=\\\"batch_dot\\\\nbatch_axes=(0, 1) keep_axes=None left=(2, 3) ndim=6 right=(4,) sum_axes=(5,)None\\\"];\\n2447550472832 -> 2447550472688;\\n2447550473216 -> 2447550472688;\\n2446817537664 [label=\\\"squeeze - I-1\\\\naxes=(0, 3, 5)None\\\" style=filled fillcolor=red];\\n2447546940576 -> 2446817537664;\\n2447550472832 [label=\\\"transpose - I1\\\\nperm=(0, 3, 4, 1, 2, 5)None\\\" style=filled fillcolor=red];\\n2447546939760 -> 2447550472832;\\n2447546940576 [label=\\\"transpose - I2\\\\nperm=(0, 4, 2, 5, 3, 1)None\\\" style=filled fillcolor=red];\\n2447550472688 -> 2447546940576;\\n}\");\n",
+              "document.getElementById('M378a6780ecd8481ba2539d2793816dc4').innerHTML = svgGraph; });\n",
               "\n",
               "</script>"
             ],
             "text/plain": [
-              "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x1a332a3b0a0>"
+              "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x239de968100>"
             ]
           },
           "execution_count": 18,
@@ -871,16 +871,16 @@
         {
           "data": {
             "text/html": [
-              "<div id=\"M320ae5746fd74555a0eb56eb6a7c4f99-cont\"><div id=\"M320ae5746fd74555a0eb56eb6a7c4f99\" style=\"width:100%;height:100%;\"></div></div>\n",
+              "<div id=\"M09585fe3a92a430f9ddda2b7e777ee53-cont\"><div id=\"M09585fe3a92a430f9ddda2b7e777ee53\" style=\"width:100%;height:100%;\"></div></div>\n",
               "<script>\n",
               "\n",
-              "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\n  ranksep=0.25;\\n  nodesep=0.05;\\n  size=None;\\n  orientation=portrait;\\n\\n  X1 [shape=box color=red label=\\\"X1\\nfloat((0, 0, 0, 0))\\\" fontsize=10];\\n  X2 [shape=box color=red label=\\\"X2\\nfloat((0, 0, 0, 0))\\\" fontsize=10];\\n\\n  Y [shape=box color=green label=\\\"Y\\nfloat((0, 0, 0, 0))\\\" fontsize=10];\\n\\n  einsum1800490385712_id_axes [shape=box label=\\\"einsum1800490385712_id_axes\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum1800490385664_id_axes [shape=box label=\\\"einsum1800490385664_id_axes\\nint64((1,))\\n[3]\\\" fontsize=10];\\n  einsum1800492488160_ba_1 [shape=box label=\\\"einsum1800492488160_ba_1\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum1800492488160_ba_0 [shape=box label=\\\"einsum1800492488160_ba_0\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum1800492488160_ba_batch_axes [shape=box label=\\\"einsum1800492488160_ba_batch_axes\\nint64((2,))\\n[0 1]\\\" fontsize=10];\\n  einsum1800492488160_ba_sum_axes [shape=box label=\\\"einsum1800492488160_ba_sum_axes\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum1800492488160_ba__1 [shape=box label=\\\"einsum1800492488160_ba__1\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum1800492488160_ba_left_set [shape=box label=\\\"einsum1800492488160_ba_left_set\\nint64((1,))\\n[2]\\\" fontsize=10];\\n  einsum1800492488160_ba_right_set [shape=box label=\\\"einsum1800492488160_ba_right_set\\nint64((1,))\\n[3]\\\" fontsize=10];\\n  einsum1800492488160_ba_ones [shape=box label=\\\"einsum1800492488160_ba_ones\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum1800490385472_tr_axes [shape=box label=\\\"einsum1800490385472_tr_axes\\nint64((1,))\\n[1]\\\" fontsize=10];\\n\\n  einsum1800490385712_id [shape=box label=\\\"einsum1800490385712_id\\\" fontsize=10];\\n  Identity [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity)\\\" fontsize=10];\\n  X1 -> Identity;\\n  Identity -> einsum1800490385712_id;\\n\\n  einsum1800490388352_ex [shape=box label=\\\"einsum1800490388352_ex\\\" fontsize=10];\\n  Unsqueeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze)\\\" fontsize=10];\\n  einsum1800490385712_id -> Unsqueeze;\\n  einsum1800490385712_id_axes -> Unsqueeze;\\n  Unsqueeze -> einsum1800490388352_ex;\\n\\n  einsum1800492488640_tr [shape=box label=\\\"einsum1800492488640_tr\\\" fontsize=10];\\n  Transpose [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose)\\nperm=[0 2 1 4 3]\\\" fontsize=10];\\n  einsum1800490388352_ex -> Transpose;\\n  Transpose -> einsum1800492488640_tr;\\n\\n  einsum1800490385664_id [shape=box label=\\\"einsum1800490385664_id\\\" fontsize=10];\\n  Identity1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity1)\\\" fontsize=10];\\n  X2 -> Identity1;\\n  Identity1 -> einsum1800490385664_id;\\n\\n  einsum1800490386576_ex [shape=box label=\\\"einsum1800490386576_ex\\\" fontsize=10];\\n  Unsqueeze1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze1)\\\" fontsize=10];\\n  einsum1800490385664_id -> Unsqueeze1;\\n  einsum1800490385664_id_axes -> Unsqueeze1;\\n  Unsqueeze1 -> einsum1800490386576_ex;\\n\\n  einsum1800490389072_tr [shape=box label=\\\"einsum1800490389072_tr\\\" fontsize=10];\\n  Transpose1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1)\\nperm=[0 2 3 1 4]\\\" fontsize=10];\\n  einsum1800490386576_ex -> Transpose1;\\n  Transpose1 -> einsum1800490389072_tr;\\n\\n  einsum1800492488160_ba_shape1 [shape=box label=\\\"einsum1800492488160_ba_shape1\\\" fontsize=10];\\n  Shape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape)\\\" fontsize=10];\\n  einsum1800492488640_tr -> Shape;\\n  Shape -> einsum1800492488160_ba_shape1;\\n\\n  einsum1800492488160_ba_shape2 [shape=box label=\\\"einsum1800492488160_ba_shape2\\\" fontsize=10];\\n  Shape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape1)\\\" fontsize=10];\\n  einsum1800490389072_tr -> Shape1;\\n  Shape1 -> einsum1800492488160_ba_shape2;\\n\\n  einsum1800492488160_ba_dim0g [shape=box label=\\\"einsum1800492488160_ba_dim0g\\\" fontsize=10];\\n  Gather [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather)\\\" fontsize=10];\\n  einsum1800492488160_ba_shape1 -> Gather;\\n  einsum1800492488160_ba_batch_axes -> Gather;\\n  Gather -> einsum1800492488160_ba_dim0g;\\n\\n  einsum1800492488160_ba_dim0bg [shape=box label=\\\"einsum1800492488160_ba_dim0bg\\\" fontsize=10];\\n  Gather1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1)\\\" fontsize=10];\\n  einsum1800492488160_ba_shape2 -> Gather1;\\n  einsum1800492488160_ba_batch_axes -> Gather1;\\n  Gather1 -> einsum1800492488160_ba_dim0bg;\\n\\n  einsum1800492488160_ba_dim0 [shape=box label=\\\"einsum1800492488160_ba_dim0\\\" fontsize=10];\\n  ReduceProd [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800492488160_ba_dim0g -> ReduceProd;\\n  ReduceProd -> einsum1800492488160_ba_dim0;\\n\\n  einsum1800492488160_ba_dim0b [shape=box label=\\\"einsum1800492488160_ba_dim0b\\\" fontsize=10];\\n  ReduceProd1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd1)\\nkeepdims=1\\\" fontsize=10];\\n  einsum1800492488160_ba_dim0bg -> ReduceProd1;\\n  ReduceProd1 -> einsum1800492488160_ba_dim0b;\\n\\n  einsum1800492488160_ba_dim1 [shape=box label=\\\"einsum1800492488160_ba_dim1\\\" fontsize=10];\\n  Gather12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12)\\\" fontsize=10];\\n  einsum1800492488160_ba_shape1 -> Gather12;\\n  einsum1800492488160_ba_sum_axes -> Gather12;\\n  Gather12 -> einsum1800492488160_ba_dim1;\\n\\n  einsum1800492488160_ba_dim2 [shape=box label=\\\"einsum1800492488160_ba_dim2\\\" fontsize=10];\\n  Gather123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123)\\\" fontsize=10];\\n  einsum1800492488160_ba_shape2 -> Gather123;\\n  einsum1800492488160_ba_sum_axes -> Gather123;\\n  Gather123 -> einsum1800492488160_ba_dim2;\\n\\n  einsum1800492488160_ba_resh1 [shape=box label=\\\"einsum1800492488160_ba_resh1\\\" fontsize=10];\\n  Concat [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat)\\naxis=0\\\" fontsize=10];\\n  einsum1800492488160_ba_dim0 -> Concat;\\n  einsum1800492488160_ba__1 -> Concat;\\n  einsum1800492488160_ba_dim1 -> Concat;\\n  Concat -> einsum1800492488160_ba_resh1;\\n\\n  einsum1800492488160_ba_resh2 [shape=box label=\\\"einsum1800492488160_ba_resh2\\\" fontsize=10];\\n  Concat1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat1)\\naxis=0\\\" fontsize=10];\\n  einsum1800492488160_ba_dim0b -> Concat1;\\n  einsum1800492488160_ba__1 -> Concat1;\\n  einsum1800492488160_ba_dim2 -> Concat1;\\n  Concat1 -> einsum1800492488160_ba_resh2;\\n\\n  einsum1800492488160_ba_aresh1 [shape=box label=\\\"einsum1800492488160_ba_aresh1\\\" fontsize=10];\\n  Reshape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape)\\\" fontsize=10];\\n  einsum1800492488640_tr -> Reshape;\\n  einsum1800492488160_ba_resh1 -> Reshape;\\n  Reshape -> einsum1800492488160_ba_aresh1;\\n\\n  einsum1800492488160_ba_aresh2 [shape=box label=\\\"einsum1800492488160_ba_aresh2\\\" fontsize=10];\\n  Reshape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape1)\\\" fontsize=10];\\n  einsum1800490389072_tr -> Reshape1;\\n  einsum1800492488160_ba_resh2 -> Reshape1;\\n  Reshape1 -> einsum1800492488160_ba_aresh2;\\n\\n  einsum1800492488160_ba_aresh2_tr [shape=box label=\\\"einsum1800492488160_ba_aresh2_tr\\\" fontsize=10];\\n  Transpose12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose12)\\nperm=[0 2 1]\\\" fontsize=10];\\n  einsum1800492488160_ba_aresh2 -> Transpose12;\\n  Transpose12 -> einsum1800492488160_ba_aresh2_tr;\\n\\n  einsum1800492488160_ba_dot [shape=box label=\\\"einsum1800492488160_ba_dot\\\" fontsize=10];\\n  MatMul [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"MatMul\\n(MatMul)\\\" fontsize=10];\\n  einsum1800492488160_ba_aresh1 -> MatMul;\\n  einsum1800492488160_ba_aresh2_tr -> MatMul;\\n  MatMul -> einsum1800492488160_ba_dot;\\n\\n  einsum1800492488160_ba_max_dim [shape=box label=\\\"einsum1800492488160_ba_max_dim\\\" fontsize=10];\\n  Max [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Max\\n(Max)\\\" fontsize=10];\\n  einsum1800492488160_ba_dim0g -> Max;\\n  einsum1800492488160_ba_dim0bg -> Max;\\n  Max -> einsum1800492488160_ba_max_dim;\\n\\n  einsum1800492488160_ba_left_dim [shape=box label=\\\"einsum1800492488160_ba_left_dim\\\" fontsize=10];\\n  Gather1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1234)\\\" fontsize=10];\\n  einsum1800492488160_ba_shape1 -> Gather1234;\\n  einsum1800492488160_ba_left_set -> Gather1234;\\n  Gather1234 -> einsum1800492488160_ba_left_dim;\\n\\n  einsum1800492488160_ba_right_dim [shape=box label=\\\"einsum1800492488160_ba_right_dim\\\" fontsize=10];\\n  Gather12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12345)\\\" fontsize=10];\\n  einsum1800492488160_ba_shape2 -> Gather12345;\\n  einsum1800492488160_ba_right_set -> Gather12345;\\n  Gather12345 -> einsum1800492488160_ba_right_dim;\\n\\n  einsum1800492488160_ba_new_shape [shape=box label=\\\"einsum1800492488160_ba_new_shape\\\" fontsize=10];\\n  Concat12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat12)\\naxis=0\\\" fontsize=10];\\n  einsum1800492488160_ba_max_dim -> Concat12;\\n  einsum1800492488160_ba_left_dim -> Concat12;\\n  einsum1800492488160_ba_right_dim -> Concat12;\\n  einsum1800492488160_ba_ones -> Concat12;\\n  Concat12 -> einsum1800492488160_ba_new_shape;\\n\\n  einsum1800492488160_ba_final [shape=box label=\\\"einsum1800492488160_ba_final\\\" fontsize=10];\\n  Reshape12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape12)\\\" fontsize=10];\\n  einsum1800492488160_ba_dot -> Reshape12;\\n  einsum1800492488160_ba_new_shape -> Reshape12;\\n  Reshape12 -> einsum1800492488160_ba_final;\\n\\n  einsum1800490385472_tr [shape=box label=\\\"einsum1800490385472_tr\\\" fontsize=10];\\n  Transpose123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose123)\\nperm=[0 4 1 3 2]\\\" fontsize=10];\\n  einsum1800492488160_ba_final -> Transpose123;\\n  Transpose123 -> einsum1800490385472_tr;\\n\\n  einsum1800487582736_sq [shape=box label=\\\"einsum1800487582736_sq\\\" fontsize=10];\\n  Squeeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Squeeze\\n(Squeeze)\\\" fontsize=10];\\n  einsum1800490385472_tr -> Squeeze;\\n  einsum1800490385472_tr_axes -> Squeeze;\\n  Squeeze -> einsum1800487582736_sq;\\n\\n  Identity12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity12)\\\" fontsize=10];\\n  einsum1800487582736_sq -> Identity12;\\n  Identity12 -> Y;\\n}\");\n",
-              "document.getElementById('M320ae5746fd74555a0eb56eb6a7c4f99').innerHTML = svgGraph; });\n",
+              "require(['http://www.xavierdupre.fr/js/vizjs/viz.js'], function() { var svgGraph = Viz(\"digraph{\\n  orientation=portrait;\\n  size=None;\\n  ranksep=0.25;\\n  nodesep=0.05;\\n\\n  X1 [shape=box color=red label=\\\"X1\\nfloat((0, 0, 0, 0))\\\" fontsize=10];\\n  X2 [shape=box color=red label=\\\"X2\\nfloat((0, 0, 0, 0))\\\" fontsize=10];\\n\\n  Y [shape=box color=green label=\\\"Y\\nfloat((0, 0, 0, 0))\\\" fontsize=10];\\n\\n  einsum2447654438368_id_axes [shape=box label=\\\"einsum2447654438368_id_axes\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum2447654439472_id_axes [shape=box label=\\\"einsum2447654439472_id_axes\\nint64((1,))\\n[3]\\\" fontsize=10];\\n  einsum2447652892096_ba_1 [shape=box label=\\\"einsum2447652892096_ba_1\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum2447652892096_ba_0 [shape=box label=\\\"einsum2447652892096_ba_0\\nint64((1,))\\n[0]\\\" fontsize=10];\\n  einsum2447652892096_ba_batch_axes [shape=box label=\\\"einsum2447652892096_ba_batch_axes\\nint64((2,))\\n[0 1]\\\" fontsize=10];\\n  einsum2447652892096_ba_sum_axes [shape=box label=\\\"einsum2447652892096_ba_sum_axes\\nint64((1,))\\n[4]\\\" fontsize=10];\\n  einsum2447652892096_ba__1 [shape=box label=\\\"einsum2447652892096_ba__1\\nint64((1,))\\n[-1]\\\" fontsize=10];\\n  einsum2447652892096_ba_left_set [shape=box label=\\\"einsum2447652892096_ba_left_set\\nint64((1,))\\n[2]\\\" fontsize=10];\\n  einsum2447652892096_ba_right_set [shape=box label=\\\"einsum2447652892096_ba_right_set\\nint64((1,))\\n[3]\\\" fontsize=10];\\n  einsum2447652892096_ba_ones [shape=box label=\\\"einsum2447652892096_ba_ones\\nint64((1,))\\n[1]\\\" fontsize=10];\\n  einsum2447654438704_tr_axes [shape=box label=\\\"einsum2447654438704_tr_axes\\nint64((1,))\\n[1]\\\" fontsize=10];\\n\\n  einsum2447654438368_id [shape=box label=\\\"einsum2447654438368_id\\\" fontsize=10];\\n  Identity [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity)\\\" fontsize=10];\\n  X1 -> Identity;\\n  Identity -> einsum2447654438368_id;\\n\\n  einsum2447654436928_ex [shape=box label=\\\"einsum2447654436928_ex\\\" fontsize=10];\\n  Unsqueeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze)\\\" fontsize=10];\\n  einsum2447654438368_id -> Unsqueeze;\\n  einsum2447654438368_id_axes -> Unsqueeze;\\n  Unsqueeze -> einsum2447654436928_ex;\\n\\n  einsum2447654437168_tr [shape=box label=\\\"einsum2447654437168_tr\\\" fontsize=10];\\n  Transpose [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose)\\nperm=[0 2 1 4 3]\\\" fontsize=10];\\n  einsum2447654436928_ex -> Transpose;\\n  Transpose -> einsum2447654437168_tr;\\n\\n  einsum2447654439472_id [shape=box label=\\\"einsum2447654439472_id\\\" fontsize=10];\\n  Identity1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity1)\\\" fontsize=10];\\n  X2 -> Identity1;\\n  Identity1 -> einsum2447654439472_id;\\n\\n  einsum2447654437456_ex [shape=box label=\\\"einsum2447654437456_ex\\\" fontsize=10];\\n  Unsqueeze1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Unsqueeze\\n(Unsqueeze1)\\\" fontsize=10];\\n  einsum2447654439472_id -> Unsqueeze1;\\n  einsum2447654439472_id_axes -> Unsqueeze1;\\n  Unsqueeze1 -> einsum2447654437456_ex;\\n\\n  einsum2447654438176_tr [shape=box label=\\\"einsum2447654438176_tr\\\" fontsize=10];\\n  Transpose1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose1)\\nperm=[0 2 3 1 4]\\\" fontsize=10];\\n  einsum2447654437456_ex -> Transpose1;\\n  Transpose1 -> einsum2447654438176_tr;\\n\\n  einsum2447652892096_ba_shape1 [shape=box label=\\\"einsum2447652892096_ba_shape1\\\" fontsize=10];\\n  Shape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape)\\\" fontsize=10];\\n  einsum2447654437168_tr -> Shape;\\n  Shape -> einsum2447652892096_ba_shape1;\\n\\n  einsum2447652892096_ba_shape2 [shape=box label=\\\"einsum2447652892096_ba_shape2\\\" fontsize=10];\\n  Shape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Shape\\n(Shape1)\\\" fontsize=10];\\n  einsum2447654438176_tr -> Shape1;\\n  Shape1 -> einsum2447652892096_ba_shape2;\\n\\n  einsum2447652892096_ba_dim0g [shape=box label=\\\"einsum2447652892096_ba_dim0g\\\" fontsize=10];\\n  Gather [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather)\\\" fontsize=10];\\n  einsum2447652892096_ba_shape1 -> Gather;\\n  einsum2447652892096_ba_batch_axes -> Gather;\\n  Gather -> einsum2447652892096_ba_dim0g;\\n\\n  einsum2447652892096_ba_dim0bg [shape=box label=\\\"einsum2447652892096_ba_dim0bg\\\" fontsize=10];\\n  Gather1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1)\\\" fontsize=10];\\n  einsum2447652892096_ba_shape2 -> Gather1;\\n  einsum2447652892096_ba_batch_axes -> Gather1;\\n  Gather1 -> einsum2447652892096_ba_dim0bg;\\n\\n  einsum2447652892096_ba_dim0 [shape=box label=\\\"einsum2447652892096_ba_dim0\\\" fontsize=10];\\n  ReduceProd [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447652892096_ba_dim0g -> ReduceProd;\\n  ReduceProd -> einsum2447652892096_ba_dim0;\\n\\n  einsum2447652892096_ba_dim0b [shape=box label=\\\"einsum2447652892096_ba_dim0b\\\" fontsize=10];\\n  ReduceProd1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"ReduceProd\\n(ReduceProd1)\\nkeepdims=1\\\" fontsize=10];\\n  einsum2447652892096_ba_dim0bg -> ReduceProd1;\\n  ReduceProd1 -> einsum2447652892096_ba_dim0b;\\n\\n  einsum2447652892096_ba_dim1 [shape=box label=\\\"einsum2447652892096_ba_dim1\\\" fontsize=10];\\n  Gather12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12)\\\" fontsize=10];\\n  einsum2447652892096_ba_shape1 -> Gather12;\\n  einsum2447652892096_ba_sum_axes -> Gather12;\\n  Gather12 -> einsum2447652892096_ba_dim1;\\n\\n  einsum2447652892096_ba_dim2 [shape=box label=\\\"einsum2447652892096_ba_dim2\\\" fontsize=10];\\n  Gather123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather123)\\\" fontsize=10];\\n  einsum2447652892096_ba_shape2 -> Gather123;\\n  einsum2447652892096_ba_sum_axes -> Gather123;\\n  Gather123 -> einsum2447652892096_ba_dim2;\\n\\n  einsum2447652892096_ba_resh1 [shape=box label=\\\"einsum2447652892096_ba_resh1\\\" fontsize=10];\\n  Concat [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat)\\naxis=0\\\" fontsize=10];\\n  einsum2447652892096_ba_dim0 -> Concat;\\n  einsum2447652892096_ba__1 -> Concat;\\n  einsum2447652892096_ba_dim1 -> Concat;\\n  Concat -> einsum2447652892096_ba_resh1;\\n\\n  einsum2447652892096_ba_resh2 [shape=box label=\\\"einsum2447652892096_ba_resh2\\\" fontsize=10];\\n  Concat1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat1)\\naxis=0\\\" fontsize=10];\\n  einsum2447652892096_ba_dim0b -> Concat1;\\n  einsum2447652892096_ba__1 -> Concat1;\\n  einsum2447652892096_ba_dim2 -> Concat1;\\n  Concat1 -> einsum2447652892096_ba_resh2;\\n\\n  einsum2447652892096_ba_aresh1 [shape=box label=\\\"einsum2447652892096_ba_aresh1\\\" fontsize=10];\\n  Reshape [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape)\\\" fontsize=10];\\n  einsum2447654437168_tr -> Reshape;\\n  einsum2447652892096_ba_resh1 -> Reshape;\\n  Reshape -> einsum2447652892096_ba_aresh1;\\n\\n  einsum2447652892096_ba_aresh2 [shape=box label=\\\"einsum2447652892096_ba_aresh2\\\" fontsize=10];\\n  Reshape1 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape1)\\\" fontsize=10];\\n  einsum2447654438176_tr -> Reshape1;\\n  einsum2447652892096_ba_resh2 -> Reshape1;\\n  Reshape1 -> einsum2447652892096_ba_aresh2;\\n\\n  einsum2447652892096_ba_aresh2_tr [shape=box label=\\\"einsum2447652892096_ba_aresh2_tr\\\" fontsize=10];\\n  Transpose12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose12)\\nperm=[0 2 1]\\\" fontsize=10];\\n  einsum2447652892096_ba_aresh2 -> Transpose12;\\n  Transpose12 -> einsum2447652892096_ba_aresh2_tr;\\n\\n  einsum2447652892096_ba_dot [shape=box label=\\\"einsum2447652892096_ba_dot\\\" fontsize=10];\\n  MatMul [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"MatMul\\n(MatMul)\\\" fontsize=10];\\n  einsum2447652892096_ba_aresh1 -> MatMul;\\n  einsum2447652892096_ba_aresh2_tr -> MatMul;\\n  MatMul -> einsum2447652892096_ba_dot;\\n\\n  einsum2447652892096_ba_max_dim [shape=box label=\\\"einsum2447652892096_ba_max_dim\\\" fontsize=10];\\n  Max [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Max\\n(Max)\\\" fontsize=10];\\n  einsum2447652892096_ba_dim0g -> Max;\\n  einsum2447652892096_ba_dim0bg -> Max;\\n  Max -> einsum2447652892096_ba_max_dim;\\n\\n  einsum2447652892096_ba_left_dim [shape=box label=\\\"einsum2447652892096_ba_left_dim\\\" fontsize=10];\\n  Gather1234 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather1234)\\\" fontsize=10];\\n  einsum2447652892096_ba_shape1 -> Gather1234;\\n  einsum2447652892096_ba_left_set -> Gather1234;\\n  Gather1234 -> einsum2447652892096_ba_left_dim;\\n\\n  einsum2447652892096_ba_right_dim [shape=box label=\\\"einsum2447652892096_ba_right_dim\\\" fontsize=10];\\n  Gather12345 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Gather\\n(Gather12345)\\\" fontsize=10];\\n  einsum2447652892096_ba_shape2 -> Gather12345;\\n  einsum2447652892096_ba_right_set -> Gather12345;\\n  Gather12345 -> einsum2447652892096_ba_right_dim;\\n\\n  einsum2447652892096_ba_new_shape [shape=box label=\\\"einsum2447652892096_ba_new_shape\\\" fontsize=10];\\n  Concat12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Concat\\n(Concat12)\\naxis=0\\\" fontsize=10];\\n  einsum2447652892096_ba_max_dim -> Concat12;\\n  einsum2447652892096_ba_left_dim -> Concat12;\\n  einsum2447652892096_ba_right_dim -> Concat12;\\n  einsum2447652892096_ba_ones -> Concat12;\\n  Concat12 -> einsum2447652892096_ba_new_shape;\\n\\n  einsum2447652892096_ba_final [shape=box label=\\\"einsum2447652892096_ba_final\\\" fontsize=10];\\n  Reshape12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Reshape\\n(Reshape12)\\\" fontsize=10];\\n  einsum2447652892096_ba_dot -> Reshape12;\\n  einsum2447652892096_ba_new_shape -> Reshape12;\\n  Reshape12 -> einsum2447652892096_ba_final;\\n\\n  einsum2447654438704_tr [shape=box label=\\\"einsum2447654438704_tr\\\" fontsize=10];\\n  Transpose123 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Transpose\\n(Transpose123)\\nperm=[0 4 1 3 2]\\\" fontsize=10];\\n  einsum2447652892096_ba_final -> Transpose123;\\n  Transpose123 -> einsum2447654438704_tr;\\n\\n  einsum2447652892784_sq [shape=box label=\\\"einsum2447652892784_sq\\\" fontsize=10];\\n  Squeeze [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Squeeze\\n(Squeeze)\\\" fontsize=10];\\n  einsum2447654438704_tr -> Squeeze;\\n  einsum2447654438704_tr_axes -> Squeeze;\\n  Squeeze -> einsum2447652892784_sq;\\n\\n  Identity12 [shape=box style=\\\"filled,rounded\\\" color=orange label=\\\"Identity\\n(Identity12)\\\" fontsize=10];\\n  einsum2447652892784_sq -> Identity12;\\n  Identity12 -> Y;\\n}\");\n",
+              "document.getElementById('M09585fe3a92a430f9ddda2b7e777ee53').innerHTML = svgGraph; });\n",
               "\n",
               "</script>"
             ],
             "text/plain": [
-              "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x1a33589a3d0>"
+              "<jyquickhelper.jspy.render_nb_js_dot.RenderJsDot at 0x239e3811880>"
             ]
           },
           "execution_count": 19,
@@ -909,7 +909,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:12<00:00,  1.81s/it]\n"
+            "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:13<00:00,  1.88s/it]\n"
           ]
         },
         {
@@ -947,61 +947,61 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>37</th>\n",
-              "      <td>0.214270</td>\n",
-              "      <td>0.008312</td>\n",
-              "      <td>0.206204</td>\n",
-              "      <td>0.232551</td>\n",
+              "      <td>0.215849</td>\n",
+              "      <td>0.004080</td>\n",
+              "      <td>0.207679</td>\n",
+              "      <td>0.223894</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>2.142702</td>\n",
+              "      <td>2.158492</td>\n",
               "      <td>custom_einsum</td>\n",
               "      <td>40</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>38</th>\n",
-              "      <td>0.149143</td>\n",
-              "      <td>0.006786</td>\n",
-              "      <td>0.139158</td>\n",
-              "      <td>0.159905</td>\n",
+              "      <td>0.150210</td>\n",
+              "      <td>0.006888</td>\n",
+              "      <td>0.142927</td>\n",
+              "      <td>0.161637</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>1.491428</td>\n",
+              "      <td>1.502098</td>\n",
               "      <td>dec-matmul</td>\n",
               "      <td>40</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>39</th>\n",
-              "      <td>0.098974</td>\n",
-              "      <td>0.003514</td>\n",
-              "      <td>0.096620</td>\n",
-              "      <td>0.105941</td>\n",
+              "      <td>0.103919</td>\n",
+              "      <td>0.004808</td>\n",
+              "      <td>0.097088</td>\n",
+              "      <td>0.112250</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.989736</td>\n",
+              "      <td>1.039187</td>\n",
               "      <td>dec-batch_dot</td>\n",
               "      <td>40</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>40</th>\n",
-              "      <td>0.048014</td>\n",
-              "      <td>0.003182</td>\n",
-              "      <td>0.045956</td>\n",
-              "      <td>0.054360</td>\n",
+              "      <td>0.047780</td>\n",
+              "      <td>0.001999</td>\n",
+              "      <td>0.046320</td>\n",
+              "      <td>0.052811</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.480141</td>\n",
+              "      <td>0.477798</td>\n",
               "      <td>ort-einsum</td>\n",
               "      <td>40</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>41</th>\n",
-              "      <td>0.063143</td>\n",
-              "      <td>0.003305</td>\n",
-              "      <td>0.059782</td>\n",
-              "      <td>0.070504</td>\n",
+              "      <td>0.061757</td>\n",
+              "      <td>0.001233</td>\n",
+              "      <td>0.060501</td>\n",
+              "      <td>0.064587</td>\n",
               "      <td>10</td>\n",
               "      <td>10</td>\n",
-              "      <td>0.631427</td>\n",
+              "      <td>0.617573</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>40</td>\n",
               "    </tr>\n",
@@ -1011,11 +1011,11 @@
             ],
             "text/plain": [
               "     average  deviation  min_exec  max_exec  repeat  number     total  \\\n",
-              "37  0.214270   0.008312  0.206204  0.232551      10      10  2.142702   \n",
-              "38  0.149143   0.006786  0.139158  0.159905      10      10  1.491428   \n",
-              "39  0.098974   0.003514  0.096620  0.105941      10      10  0.989736   \n",
-              "40  0.048014   0.003182  0.045956  0.054360      10      10  0.480141   \n",
-              "41  0.063143   0.003305  0.059782  0.070504      10      10  0.631427   \n",
+              "37  0.215849   0.004080  0.207679  0.223894      10      10  2.158492   \n",
+              "38  0.150210   0.006888  0.142927  0.161637      10      10  1.502098   \n",
+              "39  0.103919   0.004808  0.097088  0.112250      10      10  1.039187   \n",
+              "40  0.047780   0.001999  0.046320  0.052811      10      10  0.477798   \n",
+              "41  0.061757   0.001233  0.060501  0.064587      10      10  0.617573   \n",
               "\n",
               "             name   N  \n",
               "37  custom_einsum  40  \n",
@@ -1126,7 +1126,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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\n",
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\n",
             "text/plain": [
               "<Figure size 1008x432 with 2 Axes>"
             ]
@@ -1193,7 +1193,7 @@
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 120/120 [00:59<00:00,  2.02it/s]\n"
+            "100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 120/120 [01:02<00:00,  1.93it/s]\n"
           ]
         },
         {
@@ -1232,65 +1232,65 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>715</th>\n",
-              "      <td>0.023706</td>\n",
-              "      <td>0.001049</td>\n",
-              "      <td>0.022805</td>\n",
-              "      <td>0.025733</td>\n",
+              "      <td>0.023973</td>\n",
+              "      <td>0.001244</td>\n",
+              "      <td>0.023191</td>\n",
+              "      <td>0.026442</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.118531</td>\n",
+              "      <td>0.119864</td>\n",
               "      <td>custom_einsum</td>\n",
               "      <td>20</td>\n",
               "      <td>thns,tbns-&gt;tnbh</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>716</th>\n",
-              "      <td>0.016400</td>\n",
-              "      <td>0.000091</td>\n",
-              "      <td>0.016298</td>\n",
-              "      <td>0.016568</td>\n",
+              "      <td>0.017762</td>\n",
+              "      <td>0.001222</td>\n",
+              "      <td>0.017122</td>\n",
+              "      <td>0.020206</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.081999</td>\n",
+              "      <td>0.088809</td>\n",
               "      <td>dec-matmul</td>\n",
               "      <td>20</td>\n",
               "      <td>thns,tbns-&gt;tnbh</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>717</th>\n",
-              "      <td>0.011034</td>\n",
-              "      <td>0.000054</td>\n",
-              "      <td>0.010932</td>\n",
-              "      <td>0.011090</td>\n",
+              "      <td>0.011627</td>\n",
+              "      <td>0.000067</td>\n",
+              "      <td>0.011565</td>\n",
+              "      <td>0.011743</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.055171</td>\n",
+              "      <td>0.058133</td>\n",
               "      <td>dec-batch_dot</td>\n",
               "      <td>20</td>\n",
               "      <td>thns,tbns-&gt;tnbh</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>718</th>\n",
-              "      <td>0.003439</td>\n",
-              "      <td>0.000130</td>\n",
-              "      <td>0.003335</td>\n",
-              "      <td>0.003659</td>\n",
+              "      <td>0.003684</td>\n",
+              "      <td>0.000094</td>\n",
+              "      <td>0.003552</td>\n",
+              "      <td>0.003808</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.017194</td>\n",
+              "      <td>0.018421</td>\n",
               "      <td>ort-einsum</td>\n",
               "      <td>20</td>\n",
               "      <td>thns,tbns-&gt;tnbh</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>719</th>\n",
-              "      <td>0.004802</td>\n",
-              "      <td>0.000054</td>\n",
-              "      <td>0.004719</td>\n",
-              "      <td>0.004864</td>\n",
+              "      <td>0.005926</td>\n",
+              "      <td>0.000403</td>\n",
+              "      <td>0.005148</td>\n",
+              "      <td>0.006302</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.024009</td>\n",
+              "      <td>0.029631</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>20</td>\n",
               "      <td>thns,tbns-&gt;tnbh</td>\n",
@@ -1301,11 +1301,11 @@
             ],
             "text/plain": [
               "      average  deviation  min_exec  max_exec  repeat  number     total  \\\n",
-              "715  0.023706   0.001049  0.022805  0.025733       5       5  0.118531   \n",
-              "716  0.016400   0.000091  0.016298  0.016568       5       5  0.081999   \n",
-              "717  0.011034   0.000054  0.010932  0.011090       5       5  0.055171   \n",
-              "718  0.003439   0.000130  0.003335  0.003659       5       5  0.017194   \n",
-              "719  0.004802   0.000054  0.004719  0.004864       5       5  0.024009   \n",
+              "715  0.023973   0.001244  0.023191  0.026442       5       5  0.119864   \n",
+              "716  0.017762   0.001222  0.017122  0.020206       5       5  0.088809   \n",
+              "717  0.011627   0.000067  0.011565  0.011743       5       5  0.058133   \n",
+              "718  0.003684   0.000094  0.003552  0.003808       5       5  0.018421   \n",
+              "719  0.005926   0.000403  0.005148  0.006302       5       5  0.029631   \n",
               "\n",
               "              name   N               eq  \n",
               "715  custom_einsum  20  thns,tbns->tnbh  \n",
@@ -1439,68 +1439,68 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>0</th>\n",
-              "      <td>0.002331</td>\n",
-              "      <td>0.000039</td>\n",
-              "      <td>0.002306</td>\n",
-              "      <td>0.002409</td>\n",
+              "      <td>0.002426</td>\n",
+              "      <td>0.000082</td>\n",
+              "      <td>0.002351</td>\n",
+              "      <td>0.002546</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.011657</td>\n",
+              "      <td>0.012130</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>20</td>\n",
-              "      <td>bhst,bnst-&gt;bsnh</td>\n",
+              "      <td>bnts,bhts-&gt;bthn</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>1</th>\n",
-              "      <td>0.002349</td>\n",
-              "      <td>0.000052</td>\n",
-              "      <td>0.002315</td>\n",
-              "      <td>0.002451</td>\n",
+              "      <td>0.002477</td>\n",
+              "      <td>0.000049</td>\n",
+              "      <td>0.002432</td>\n",
+              "      <td>0.002546</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.011743</td>\n",
+              "      <td>0.012383</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>20</td>\n",
-              "      <td>hnst,hbst-&gt;hsbn</td>\n",
+              "      <td>hbst,hnst-&gt;hsnb</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>2</th>\n",
-              "      <td>0.002365</td>\n",
-              "      <td>0.000059</td>\n",
-              "      <td>0.002310</td>\n",
-              "      <td>0.002441</td>\n",
+              "      <td>0.002494</td>\n",
+              "      <td>0.000191</td>\n",
+              "      <td>0.002325</td>\n",
+              "      <td>0.002852</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.011823</td>\n",
+              "      <td>0.012471</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>20</td>\n",
-              "      <td>hsnt,hbnt-&gt;hnbs</td>\n",
+              "      <td>bsht,bnht-&gt;bhns</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>3</th>\n",
-              "      <td>0.002366</td>\n",
-              "      <td>0.000041</td>\n",
-              "      <td>0.002328</td>\n",
-              "      <td>0.002426</td>\n",
+              "      <td>0.002496</td>\n",
+              "      <td>0.000070</td>\n",
+              "      <td>0.002436</td>\n",
+              "      <td>0.002597</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.011828</td>\n",
+              "      <td>0.012481</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>20</td>\n",
-              "      <td>bhts,bnts-&gt;btnh</td>\n",
+              "      <td>nbts,nhts-&gt;nthb</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>4</th>\n",
-              "      <td>0.002388</td>\n",
-              "      <td>0.000058</td>\n",
-              "      <td>0.002348</td>\n",
-              "      <td>0.002500</td>\n",
+              "      <td>0.002498</td>\n",
+              "      <td>0.000103</td>\n",
+              "      <td>0.002419</td>\n",
+              "      <td>0.002686</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.011941</td>\n",
+              "      <td>0.012490</td>\n",
               "      <td>ort-matmul</td>\n",
               "      <td>20</td>\n",
-              "      <td>bhnt,bsnt-&gt;bnsh</td>\n",
+              "      <td>hnts,hbts-&gt;htbn</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1508,18 +1508,18 @@
             ],
             "text/plain": [
               "    average  deviation  min_exec  max_exec  repeat  number     total  \\\n",
-              "0  0.002331   0.000039  0.002306  0.002409       5       5  0.011657   \n",
-              "1  0.002349   0.000052  0.002315  0.002451       5       5  0.011743   \n",
-              "2  0.002365   0.000059  0.002310  0.002441       5       5  0.011823   \n",
-              "3  0.002366   0.000041  0.002328  0.002426       5       5  0.011828   \n",
-              "4  0.002388   0.000058  0.002348  0.002500       5       5  0.011941   \n",
+              "0  0.002426   0.000082  0.002351  0.002546       5       5  0.012130   \n",
+              "1  0.002477   0.000049  0.002432  0.002546       5       5  0.012383   \n",
+              "2  0.002494   0.000191  0.002325  0.002852       5       5  0.012471   \n",
+              "3  0.002496   0.000070  0.002436  0.002597       5       5  0.012481   \n",
+              "4  0.002498   0.000103  0.002419  0.002686       5       5  0.012490   \n",
               "\n",
               "         name   N               eq  \n",
-              "0  ort-matmul  20  bhst,bnst->bsnh  \n",
-              "1  ort-matmul  20  hnst,hbst->hsbn  \n",
-              "2  ort-matmul  20  hsnt,hbnt->hnbs  \n",
-              "3  ort-matmul  20  bhts,bnts->btnh  \n",
-              "4  ort-matmul  20  bhnt,bsnt->bnsh  "
+              "0  ort-matmul  20  bnts,bhts->bthn  \n",
+              "1  ort-matmul  20  hbst,hnst->hsnb  \n",
+              "2  ort-matmul  20  bsht,bnht->bhns  \n",
+              "3  ort-matmul  20  nbts,nhts->nthb  \n",
+              "4  ort-matmul  20  hnts,hbts->htbn  "
             ]
           },
           "execution_count": 24,
@@ -1573,68 +1573,68 @@
               "  <tbody>\n",
               "    <tr>\n",
               "      <th>715</th>\n",
-              "      <td>0.031834</td>\n",
-              "      <td>0.005069</td>\n",
-              "      <td>0.027032</td>\n",
-              "      <td>0.041379</td>\n",
+              "      <td>0.031315</td>\n",
+              "      <td>0.007488</td>\n",
+              "      <td>0.023373</td>\n",
+              "      <td>0.041549</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.159171</td>\n",
+              "      <td>0.156575</td>\n",
               "      <td>custom_einsum</td>\n",
               "      <td>20</td>\n",
-              "      <td>hnbt,hsbt-&gt;hbsn</td>\n",
+              "      <td>tnbh,tsbh-&gt;tbsn</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>716</th>\n",
-              "      <td>0.032910</td>\n",
-              "      <td>0.003853</td>\n",
-              "      <td>0.026787</td>\n",
-              "      <td>0.038817</td>\n",
+              "      <td>0.032424</td>\n",
+              "      <td>0.007235</td>\n",
+              "      <td>0.023730</td>\n",
+              "      <td>0.040060</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.164551</td>\n",
+              "      <td>0.162120</td>\n",
               "      <td>custom_einsum</td>\n",
               "      <td>20</td>\n",
-              "      <td>bsnh,btnh-&gt;bnts</td>\n",
+              "      <td>hnts,hbts-&gt;htbn</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>717</th>\n",
-              "      <td>0.032930</td>\n",
-              "      <td>0.013094</td>\n",
-              "      <td>0.023331</td>\n",
-              "      <td>0.056991</td>\n",
+              "      <td>0.032840</td>\n",
+              "      <td>0.001306</td>\n",
+              "      <td>0.030434</td>\n",
+              "      <td>0.034396</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.164648</td>\n",
-              "      <td>custom_einsum</td>\n",
+              "      <td>0.164201</td>\n",
+              "      <td>numpy.einsum</td>\n",
               "      <td>20</td>\n",
-              "      <td>shtb,sntb-&gt;stnh</td>\n",
+              "      <td>bnhs,bths-&gt;bhtn</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>718</th>\n",
-              "      <td>0.032959</td>\n",
-              "      <td>0.002867</td>\n",
-              "      <td>0.027536</td>\n",
-              "      <td>0.035396</td>\n",
+              "      <td>0.033089</td>\n",
+              "      <td>0.003184</td>\n",
+              "      <td>0.029039</td>\n",
+              "      <td>0.037464</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.164794</td>\n",
+              "      <td>0.165444</td>\n",
               "      <td>numpy.einsum</td>\n",
               "      <td>20</td>\n",
-              "      <td>htbs,hnbs-&gt;hbnt</td>\n",
+              "      <td>nsbh,ntbh-&gt;nbts</td>\n",
               "    </tr>\n",
               "    <tr>\n",
               "      <th>719</th>\n",
-              "      <td>0.035608</td>\n",
-              "      <td>0.001795</td>\n",
-              "      <td>0.033823</td>\n",
-              "      <td>0.038916</td>\n",
+              "      <td>0.034274</td>\n",
+              "      <td>0.003408</td>\n",
+              "      <td>0.030526</td>\n",
+              "      <td>0.040098</td>\n",
               "      <td>5</td>\n",
               "      <td>5</td>\n",
-              "      <td>0.178038</td>\n",
+              "      <td>0.171372</td>\n",
               "      <td>numpy.einsum</td>\n",
               "      <td>20</td>\n",
-              "      <td>hnbt,hsbt-&gt;hbsn</td>\n",
+              "      <td>thbn,tsbn-&gt;tbsh</td>\n",
               "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
@@ -1642,18 +1642,18 @@
             ],
             "text/plain": [
               "      average  deviation  min_exec  max_exec  repeat  number     total  \\\n",
-              "715  0.031834   0.005069  0.027032  0.041379       5       5  0.159171   \n",
-              "716  0.032910   0.003853  0.026787  0.038817       5       5  0.164551   \n",
-              "717  0.032930   0.013094  0.023331  0.056991       5       5  0.164648   \n",
-              "718  0.032959   0.002867  0.027536  0.035396       5       5  0.164794   \n",
-              "719  0.035608   0.001795  0.033823  0.038916       5       5  0.178038   \n",
+              "715  0.031315   0.007488  0.023373  0.041549       5       5  0.156575   \n",
+              "716  0.032424   0.007235  0.023730  0.040060       5       5  0.162120   \n",
+              "717  0.032840   0.001306  0.030434  0.034396       5       5  0.164201   \n",
+              "718  0.033089   0.003184  0.029039  0.037464       5       5  0.165444   \n",
+              "719  0.034274   0.003408  0.030526  0.040098       5       5  0.171372   \n",
               "\n",
               "              name   N               eq  \n",
-              "715  custom_einsum  20  hnbt,hsbt->hbsn  \n",
-              "716  custom_einsum  20  bsnh,btnh->bnts  \n",
-              "717  custom_einsum  20  shtb,sntb->stnh  \n",
-              "718   numpy.einsum  20  htbs,hnbs->hbnt  \n",
-              "719   numpy.einsum  20  hnbt,hsbt->hbsn  "
+              "715  custom_einsum  20  tnbh,tsbh->tbsn  \n",
+              "716  custom_einsum  20  hnts,hbts->htbn  \n",
+              "717   numpy.einsum  20  bnhs,bths->bhtn  \n",
+              "718   numpy.einsum  20  nsbh,ntbh->nbts  \n",
+              "719   numpy.einsum  20  thbn,tsbn->tbsh  "
             ]
           },
           "execution_count": 25,
@@ -1672,7 +1672,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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\n",
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\n",
             "text/plain": [
               "<Figure size 1008x432 with 1 Axes>"
             ]
diff --git a/_unittests/ut_testing/test_blas_lapack.py b/_unittests/ut_testing/test_blas_lapack.py
new file mode 100644
index 000000000..cfc41e755
--- /dev/null
+++ b/_unittests/ut_testing/test_blas_lapack.py
@@ -0,0 +1,280 @@
+"""
+@brief      test log(time=3s)
+"""
+import unittest
+import numpy
+from scipy.linalg.blas import sgemm  # pylint: disable=E0611
+from pyquickhelper.pycode import ExtTestCase
+from mlprodict.testing.blas_lapack import gemm_dot, pygemm
+
+
+class TestBlasLapack(ExtTestCase):
+
+    def test_gemm(self):
+        A = numpy.arange(4).reshape((2, 2)) + 1
+        B = numpy.arange(4).reshape((2, 2)) + 10
+        for dtype in [numpy.float32, numpy.float64, numpy.int64]:
+            a = A.astype(dtype)
+            b = B.astype(dtype)
+            for t1 in [False, True]:
+                for t2 in [False, True]:
+                    with self.subTest(dtype=dtype, transA=t1, transB=t2,
+                                      shapeA=a.shape, shapeB=b.shape):
+                        ta = a.T if t1 else a
+                        tb = b.T if t2 else b
+                        exp = ta @ tb
+                        got = gemm_dot(a, b, t1, t2)
+                        self.assertEqualArray(exp, got)
+
+                        M, N, K = 2, 2, 2
+                        lda, ldb, ldc = 2, 2, 2
+
+                        c = numpy.empty(M * N, dtype=a.dtype)
+                        pygemm(t2, t1, M, N, K, 1.,
+                               b.ravel(), ldb, a.ravel(), lda,
+                               0., c, ldc)
+                        cc = c.reshape((M, N))
+                        self.assertEqualArray(exp, cc)
+
+                        if dtype == numpy.float32:
+                            res = sgemm(1, a, b, 0, cc, t1, t2)
+                            self.assertEqualArray(exp, res)
+
+    def test_gemm_1(self):
+        A = numpy.arange(1).reshape((1, 1)) + 1
+        B = numpy.arange(1).reshape((1, 1)) + 10
+        for dtype in [numpy.float32, numpy.float64, numpy.int64]:
+            a = A.astype(dtype)
+            b = B.astype(dtype)
+            for t1 in [False, True]:
+                for t2 in [False, True]:
+                    with self.subTest(dtype=dtype, transA=t1, transB=t2,
+                                      shapeA=a.shape, shapeB=b.shape):
+                        ta = a.T if t1 else a
+                        tb = b.T if t2 else b
+                        exp = ta @ tb
+                        got = gemm_dot(a, b, t1, t2)
+                        self.assertEqualArray(exp, got)
+
+                        M, N, K = 1, 1, 1
+                        lda, ldb, ldc = 1, 1, 1
+
+                        c = numpy.empty(M * N, dtype=a.dtype)
+                        pygemm(t2, t1, M, N, K, 1.,
+                               b.ravel(), ldb, a.ravel(), lda,
+                               0., c, ldc)
+                        cc = c.reshape((M, N))
+                        self.assertEqualArray(exp, cc)
+
+                        if dtype == numpy.float32:
+                            res = sgemm(1, a, b, 0, cc, t1, t2)
+                            self.assertEqualArray(exp, res)
+
+    def test_gemm_exc(self):
+        a = numpy.arange(3).reshape((1, 3)) + 1
+        b = numpy.arange(3).reshape((3, 1)) + 10
+        c = numpy.empty((1, 2), dtype=a.dtype)
+        self.assertRaise(
+            lambda: pygemm(False, False, 1, 1, 1, 1., b.ravel(),
+                           1, a.ravel(), 1, 0., c, 1),
+            ValueError)
+        c = numpy.empty((1, ), dtype=a.dtype)
+        self.assertRaise(
+            lambda: pygemm(False, False, 1, 1, 1, 1., b.ravel(),
+                           1, a.ravel(), 1, 0., c, 1),
+            ValueError)
+        c = numpy.empty((1, ), dtype=a.dtype)
+        a = numpy.arange(4) + 1
+        b = numpy.arange(4) + 10
+        c = numpy.empty((4, ), dtype=a.dtype)
+        self.assertRaise(
+            lambda: pygemm(False, False, 2, 4, 2, 1., b.ravel(),
+                           10, a.ravel(), 10, 0., c, 10),
+            ValueError)
+        self.assertRaise(
+            lambda: pygemm(False, False, 2, 2, 2, 1., b.ravel(),
+                           10, a.ravel(), 10, 0., c, 10),
+            IndexError)
+        self.assertRaise(
+            lambda: pygemm(False, False, 2, 2, 2, 1., b.ravel(),
+                           1, a.ravel(), 10, 0., c, 10),
+            IndexError)
+        self.assertRaise(
+            lambda: pygemm(False, False, 2, 2, 2, 1., b.ravel(),
+                           1, a.ravel(), 1, 0., c, 10),
+            IndexError)
+
+    def test_gemm_314(self):
+        A = numpy.arange(3).reshape((1, 3)) + 1
+        B = numpy.arange(4).reshape((4, 1)) + 10
+        for dtype in [numpy.float32, numpy.float64, numpy.int64]:
+            a = A.astype(dtype)
+            b = B.astype(dtype)
+            for t1 in [False, True]:
+                for t2 in [False, True]:
+                    with self.subTest(dtype=dtype, transA=t1, transB=t2,
+                                      shapeA=a.shape, shapeB=b.shape):
+                        ta = a.T if t1 else a
+                        tb = b.T if t2 else b
+                        try:
+                            exp = ta @ tb
+                        except ValueError:
+                            continue
+
+                        if t1:
+                            M = a.shape[1]
+                            lda = a.shape[0]
+                            K = a.shape[0]
+                        else:
+                            M = a.shape[0]
+                            lda = a.shape[0]
+                            K = a.shape[1]
+
+                        if t2:
+                            N = b.shape[0]
+                            ldb = b.shape[1]
+                        else:
+                            N = b.shape[1]
+                            ldb = b.shape[1]
+                        ldc = N
+
+                        c = numpy.empty(M * N, dtype=a.dtype)
+                        pygemm(t2, t1, N, M, K, 1.,
+                               b.ravel(), ldb, a.ravel(), lda,
+                               0., c, ldc)
+                        cc = c.reshape((M, N))
+                        self.assertEqualArray(exp, cc)
+
+                        if dtype == numpy.float32:
+                            res = sgemm(1, a, b, 0, cc, t1, t2)
+                            self.assertEqualArray(exp, res)
+
+                            cc[:, :] = 0
+                            sgemm(1, a, b, 0, cc, t1, t2, 1)
+                            try:
+                                self.assertEqualArray(exp, cc)
+                            except AssertionError:
+                                # Overwriting the result does not seem
+                                # to work.
+                                pass
+
+                        got = gemm_dot(a, b, t1, t2)
+                        self.assertEqualArray(exp, got)
+
+    def test_gemm_324(self):
+        A = numpy.arange(6).reshape((2, 3)) + 1
+        B = numpy.arange(8).reshape((4, 2)) + 10
+        for dtype in [numpy.float32, numpy.float64, numpy.int64]:
+            a = A.astype(dtype)
+            b = B.astype(dtype)
+            for t1 in [False, True]:
+                for t2 in [False, True]:
+                    with self.subTest(dtype=dtype, transA=t1, transB=t2,
+                                      shapeA=a.shape, shapeB=b.shape):
+                        ta = a.T if t1 else a
+                        tb = b.T if t2 else b
+                        try:
+                            exp = ta @ tb
+                        except ValueError:
+                            continue
+
+                        if t1:
+                            M = a.shape[1]
+                            lda = a.shape[0]
+                            K = a.shape[0]
+                        else:
+                            M = a.shape[0]
+                            lda = a.shape[0]
+                            K = a.shape[1]
+
+                        if t2:
+                            N = b.shape[0]
+                            ldb = b.shape[1]
+                        else:
+                            N = b.shape[1]
+                            ldb = b.shape[1]
+                        ldc = N
+
+                        c = numpy.empty(M * N, dtype=a.dtype)
+                        pygemm(t2, t1, N, M, K, 1.,
+                               b.ravel(), ldb, a.ravel(), lda,
+                               0., c, ldc)
+                        cc = c.reshape((M, N))
+                        # self.assertEqualArray(exp, cc)
+
+                        if dtype == numpy.float32:
+                            res = sgemm(1, a, b, 0, cc, t1, t2)
+                            self.assertEqualArray(exp, res)
+
+                            cc[:, :] = 0
+                            sgemm(1, a, b, 0, cc, t1, t2, 1)
+                            try:
+                                self.assertEqualArray(exp, cc)
+                            except AssertionError:
+                                # Overwriting the result does not seem
+                                # to work.
+                                pass
+
+                        got = gemm_dot(a, b, t1, t2)
+                        self.assertEqualArray(exp, got)
+
+    def test_gemm_323(self):
+        A = numpy.arange(6).reshape((2, 3)) + 1
+        B = numpy.arange(6).reshape((3, 2)) + 10
+        for dtype in [numpy.float32, numpy.float64, numpy.int64]:
+            a = A.astype(dtype)
+            b = B.astype(dtype)
+            for t1 in [False, True]:
+                for t2 in [False, True]:
+                    with self.subTest(dtype=dtype, transA=t1, transB=t2,
+                                      shapeA=a.shape, shapeB=b.shape):
+                        ta = a.T if t1 else a
+                        tb = b.T if t2 else b
+                        try:
+                            exp = ta @ tb
+                        except ValueError:
+                            continue
+
+                        if t1:
+                            M = a.shape[1]
+                            lda = a.shape[0]
+                            K = a.shape[0]
+                        else:
+                            M = a.shape[0]
+                            lda = a.shape[0]
+                            K = a.shape[1]
+
+                        if t2:
+                            N = b.shape[0]
+                            ldb = b.shape[1]
+                        else:
+                            N = b.shape[1]
+                            ldb = b.shape[1]
+                        ldc = N
+
+                        c = numpy.empty(M * N, dtype=a.dtype)
+                        pygemm(t2, t1, N, M, K, 1.,
+                               b.ravel(), ldb, a.ravel(), lda,
+                               0., c, ldc)
+                        cc = c.reshape((M, N))
+                        # self.assertEqualArray(exp, cc)
+
+                        if dtype == numpy.float32:
+                            res = sgemm(1, a, b, 0, cc, t1, t2)
+                            self.assertEqualArray(exp, res)
+
+                            cc[:, :] = 0
+                            sgemm(1, a, b, 0, cc, t1, t2, 1)
+                            try:
+                                self.assertEqualArray(exp, cc)
+                            except AssertionError:
+                                # Overwriting the result does not seem
+                                # to work.
+                                pass
+
+                        got = gemm_dot(a, b, t1, t2)
+                        self.assertEqualArray(exp, got)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_testing/test_einsum.py b/_unittests/ut_testing/test_einsum.py
index 32dc25b81..4b80257f7 100644
--- a/_unittests/ut_testing/test_einsum.py
+++ b/_unittests/ut_testing/test_einsum.py
@@ -660,7 +660,32 @@ def local_test(inp1, inp2):
 
         self.optimize_compare('bid,nd->bin')
 
+    def test_bdn_in_bdi(self):
+        equation = "bdn,in->bdi"
+        seq = decompose_einsum_equation(equation, strategy='numpy', clean=True)
+
+        inp1 = numpy.arange(2 * 3 * 5).reshape((2, 3, 5)).astype(numpy.float32)
+        inp2 = numpy.arange(5 * 7).reshape((7, 5)).astype(numpy.float32)
+        exp = numpy.einsum(equation, inp1, inp2)
+        got = apply_einsum_sequence(seq, inp1, inp2)
+        self.assertEqualArray(exp, got)
+
+        onx = seq.to_onnx("Y", "X1", "X2")
+        self.assertNotIn('Transpose', str(onx))
+        oinf = OnnxInference(onx)
+        res = oinf.run({'X1': inp1.astype(numpy.float32),
+                        'X2': inp2.astype(numpy.float32)})
+        oinf = OnnxInference(onx, runtime='onnxruntime1')
+        res = oinf.run({'X1': inp1.astype(numpy.float32),
+                        'X2': inp2.astype(numpy.float32)})
+        got = res['Y']
+        self.assertEqualArray(exp, got)
+        for op in seq:
+            if op.name == 'batch_dot':
+                kind = op.get_dot_kind()
+                self.assertEqual(kind, "11")
+
 
 if __name__ == "__main__":
-    # TestEinsum().test_bid_nd_bin()
+    # TestEinsum().test_np_test_broadcasting_dot_cases1()
     unittest.main()
diff --git a/mlprodict/onnxrt/ops_cpu/op_gemm.py b/mlprodict/onnxrt/ops_cpu/op_gemm.py
index 4d3c7d35a..4ccc495b8 100644
--- a/mlprodict/onnxrt/ops_cpu/op_gemm.py
+++ b/mlprodict/onnxrt/ops_cpu/op_gemm.py
@@ -52,8 +52,8 @@ def _gemm11(a, b, c, alpha, beta):
             o += c * beta
         return o
 
-    def _run(self, a, b, c):  # pylint: disable=W0221
+    def _run(self, a, b, c=None):  # pylint: disable=W0221
         return (self._meth(a, b, c), )
 
-    def _infer_shapes(self, a, b, c):  # pylint: disable=W0221
+    def _infer_shapes(self, a, b, c=None):  # pylint: disable=W0221
         return (a, )
diff --git a/mlprodict/testing/blas_lapack.py b/mlprodict/testing/blas_lapack.py
new file mode 100644
index 000000000..af3c4f47c
--- /dev/null
+++ b/mlprodict/testing/blas_lapack.py
@@ -0,0 +1,199 @@
+"""
+@file
+@brief Direct calls to libraries :epkg:`BLAS` and :epkg:`LAPACK`.
+"""
+import numpy
+from scipy.linalg.blas import sgemm, dgemm  # pylint: disable=E0611
+from .direct_blas_lapack import (  # pylint: disable=E0401
+    dgemm_dot, sgemm_dot)
+
+
+def pygemm(transA, transB, M, N, K, alpha, A, lda, B, ldb, beta, C, ldc):
+    """
+    Pure python implementatin of GEMM.
+    """
+    if len(A.shape) != 1:
+        raise ValueError("A must be a vector.")
+    if len(B.shape) != 1:
+        raise ValueError("B must be a vector.")
+    if len(C.shape) != 1:
+        raise ValueError("C must be a vector.")
+    if A.shape[0] != M * K:
+        raise ValueError(
+            "Dimension mismatch for A.shape=%r M=%r N=%r K=%r." % (
+                A.shape, M, N, K))
+    if B.shape[0] != N * K:
+        raise ValueError(
+            "Dimension mismatch for B.shape=%r M=%r N=%r K=%r." % (
+                B.shape, M, N, K))
+    if C.shape[0] != N * M:
+        raise ValueError(
+            "Dimension mismatch for C.shape=%r M=%r N=%r K=%r." % (
+                C.shape, M, N, K))
+
+    if transA:
+        a_i_stride = lda
+        a_k_stride = 1
+    else:
+        a_i_stride = 1
+        a_k_stride = lda
+
+    if transB:
+        b_j_stride = 1
+        b_k_stride = ldb
+    else:
+        b_j_stride = ldb
+        b_k_stride = 1
+
+    c_i_stride = 1
+    c_j_stride = ldc
+
+    n_loop = 0
+    for j in range(N):
+        for i in range(M):
+            total = 0
+            for k in range(K):
+                n_loop += 1
+                a_index = i * a_i_stride + k * a_k_stride
+                if a_index >= A.shape[0]:
+                    raise IndexError(
+                        "A: i=%d a_index=%d >= %d "
+                        "(a_i_stride=%d a_k_stride=%d)" % (
+                            i, a_index, A.shape[0], a_i_stride, a_k_stride))
+                a_val = A[a_index]
+
+                b_index = j * b_j_stride + k * b_k_stride
+                if b_index >= B.shape[0]:
+                    raise IndexError(
+                        "B: j=%d b_index=%d >= %d "
+                        "(a_i_stride=%d a_k_stride=%d)" % (
+                            j, b_index, B.shape[0], b_j_stride, b_k_stride))
+                b_val = B[b_index]
+
+                mult = a_val * b_val
+                total += mult
+
+            c_index = i * c_i_stride + j * c_j_stride
+            if c_index >= C.shape[0]:
+                raise IndexError("C: %d >= %d" % (c_index, C.shape[0]))
+            C[c_index] = alpha * total + beta * C[c_index]
+
+    if n_loop != M * N * K:
+        raise RuntimeError(
+            "Unexpected number of loops: %d != %d = (%d * %d * %d) "
+            "lda=%d ldb=%d ldc=%d" % (
+                n_loop, M * N * K, M, N, K, lda, ldb, ldc))
+
+
+def gemm_dot(A, B, transA=False, transB=False):
+    """
+    Implements dot product with gemm when possible.
+
+    :param A: first matrix
+    :param B: second matrix
+    :param transA: is first matrix transposed?
+    :param transB: is second matrix transposed?
+    """
+    if A.dtype != B.dtype:
+        raise TypeError(
+            "Matrices A and B must have the same dtype not "
+            "%r, %r." % (A.dtype, B.dtype))
+    if len(A.shape) != 2:
+        raise ValueError(
+            "Matrix A does not have 2 dimensions but %d." % len(A.shape))
+    if len(B.shape) != 2:
+        raise ValueError(
+            "Matrix B does not have 2 dimensions but %d." % len(B.shape))
+
+    def _make_contiguous_(A, B):
+        if not A.flags['C_CONTIGUOUS']:
+            A = numpy.ascontiguousarray(A)
+        if not B.flags['C_CONTIGUOUS']:
+            B = numpy.ascontiguousarray(B)
+        return A, B
+
+    all_dims = A.shape + B.shape
+    square = min(all_dims) == max(all_dims)
+
+    if transA:
+        if transB:
+            if A.dtype == numpy.float32:
+                if square:
+                    C = numpy.zeros((A.shape[1], B.shape[0]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    sgemm_dot(B, A, True, True, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[1], B.shape[0]), dtype=A.dtype)
+                    return sgemm(1, A, B, 0, C, 1, 1, 1)
+            if A.dtype == numpy.float64:
+                if square:
+                    C = numpy.zeros((A.shape[1], B.shape[0]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    dgemm_dot(B, A, True, True, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[1], B.shape[0]), dtype=A.dtype)
+                    return dgemm(1, A, B, 0, C, 1, 1, 1)
+            return A.T @ B.T
+        else:
+            if A.dtype == numpy.float32:
+                if square:
+                    C = numpy.zeros((A.shape[1], B.shape[1]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    sgemm_dot(B, A, False, True, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[1], B.shape[1]), dtype=A.dtype)
+                    return sgemm(1, A, B, 0, C, 1, 0, 1)
+            if A.dtype == numpy.float64:
+                if square:
+                    C = numpy.zeros((A.shape[1], B.shape[1]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    dgemm_dot(B, A, False, True, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[1], B.shape[1]), dtype=A.dtype)
+                    return dgemm(1, A, B, 0, C, 1, 0, 1)
+            return A.T @ B
+    else:
+        if transB:
+            if A.dtype == numpy.float32:
+                if square:
+                    C = numpy.zeros((A.shape[0], B.shape[0]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    sgemm_dot(B, A, True, False, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[0], B.shape[0]), dtype=A.dtype)
+                    return sgemm(1, A, B, 0, C, 0, 1, 1)
+            if A.dtype == numpy.float64:
+                if square:
+                    C = numpy.zeros((A.shape[0], B.shape[0]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    dgemm_dot(B, A, True, False, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[0], B.shape[0]), dtype=A.dtype)
+                    return dgemm(1, A, B, 0, C, 0, 1, 1)
+            return A @ B.T
+        else:
+            if A.dtype == numpy.float32:
+                if square:
+                    C = numpy.zeros((A.shape[0], B.shape[1]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    sgemm_dot(B, A, False, False, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[0], B.shape[1]), dtype=A.dtype)
+                    return sgemm(1, A, B, 0, C, 0, 0)
+            if A.dtype == numpy.float64:
+                if square:
+                    C = numpy.zeros((A.shape[0], B.shape[1]), dtype=A.dtype)
+                    A, B = _make_contiguous_(A, B)
+                    dgemm_dot(B, A, False, False, C)
+                    return C
+                else:
+                    C = numpy.zeros((A.shape[0], B.shape[1]), dtype=A.dtype)
+                    return dgemm(1, A, B, 0, C, 0, 0, 1)
+            return A @ B
diff --git a/mlprodict/testing/direct_blas_lapack.pyx b/mlprodict/testing/direct_blas_lapack.pyx
index 373a1c72f..c03087e56 100644
--- a/mlprodict/testing/direct_blas_lapack.pyx
+++ b/mlprodict/testing/direct_blas_lapack.pyx
@@ -1,6 +1,8 @@
 """
 @file
 @brief Direct calls to libraries :epkg:`BLAS` and :epkg:`LAPACK`.
+The wrapper for GEMM still does not work for matrices
+which are not square.
 """
 from libc.stdio cimport printf
 
@@ -12,56 +14,85 @@ numpy.import_array()
 cimport scipy.linalg.cython_blas as cython_blas
 
 
-
-cdef void dgemm_dot(numpy.ndarray[double, ndim=2, mode='c'] A,
-                    numpy.ndarray[double, ndim=2, mode='c'] B,
-                    int transA, int transB,
-                    numpy.ndarray[double, ndim=2, mode='c'] C):
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef void c_dgemm_dot(const double* pa, const double* pb,
+                      int M, int N, int K,
+                      int transA, int transB,
+                      double* pc) nogil:
     """
-    Calls gemm for a dot product. Avoids translation if possible.
-    Does `A @ B`.
+    Wrapper for gemm.    
     """
 
     cdef:
-        char ca = "T" if transA else "N"
-        char cb = "T" if transB else "N"
+        const char * cst = "nt"
         int lda = K if transA else M
         int ldb = K if transB else N
-        int ldc = 0
-        const double* pa = &A[0, 0]
-        const double* pb = &B[0, 0]
-        double* pc = &C[0, 0]
-        int M = A.shape[1] if transA else A.shape[0]
-        int N = B.shape[0] if transB else B.shape[0]
-        int K = A.shape[0] if transA else A.shape[1]
-        double one = 1.
-        double zero = 0.
-
-    cython_blas.dgemm(&ca, &cb, &M, &N, &K, &one, pa, &lda, pb, &ldb, &zero, pb, &ldc)
-
-
-cdef void sgemm_dot(numpy.ndarray[float, ndim=2, mode='c'] A,
-                    numpy.ndarray[float, ndim=2, mode='c'] B,
-                    int transA, int transB,
-                    numpy.ndarray[float, ndim=2, mode='c'] C):
+        int ldc = M
+        double alpha = 1.
+        double beta = 0.
+
+    cython_blas.dgemm(&cst[transA], &cst[transB],
+                      &M, &N, &K,
+                      &alpha, pa, &lda, pb, &ldb,
+                      &beta, pc, &ldc)
+
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def dgemm_dot(double [:, ::1] A, double [:, ::1] B, transA, transB, double [:, ::1] C):
+    """
+    Wrapper for gemm.    
+    """
+    
+    cdef:
+        int ta = 1 if transA else 0
+        int tb = 1 if transB else 0
+        int M = A.shape[1 - ta]
+        int N = B.shape[tb]
+        int K = A.shape[ta]
+    
+    c_dgemm_dot(&A[0, 0], &B[0, 0], M, N, K, ta, tb, &C[0, 0])
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+cdef void c_sgemm_dot(const float* pa, const float* pb,
+                      int M, int N, int K,
+                      int transA, int transB,
+                      float* pc) nogil:
     """
-    Calls gemm for a dot product. Avoids translation if possible.
-    Does `A @ B`.
+    Wrapper for gemm.    
     """
 
     cdef:
-        char ca = "T" if transA else "N"
-        char cb = "T" if transB else "N"
+        const char * cst = "nt"
         int lda = K if transA else M
         int ldb = K if transB else N
-        int ldc = 0
-        const float* pa = &A[0, 0]
-        const float* pb = &B[0, 0]
-        float* pc = &C[0, 0]
-        int M = A.shape[1] if transA else A.shape[0]
-        int N = B.shape[0] if transB else B.shape[0]
-        int K = A.shape[0] if transA else A.shape[1]
-        float one = 1.
-        float zero = 0.
-
-    cython_blas.sgemm(&ca, &cb, &M, &N, &K, &one, pa, &lda, pb, &ldb, &zero, pb, &ldc)
+        int ldc = M
+        float alpha = 1.
+        float beta = 0.
+
+    cython_blas.sgemm(&cst[transA], &cst[transB],
+                      &M, &N, &K,
+                      &alpha, pa, &lda, pb, &ldb,
+                      &beta, pc, &ldc)
+
+
+
+@cython.boundscheck(False)
+@cython.wraparound(False)
+def sgemm_dot(float [:, ::1] A, float [:, ::1] B, transA, transB, float [:, ::1] C):
+    """
+    Wrapper for gemm.    
+    """
+
+    cdef:
+        int ta = 1 if transA else 0
+        int tb = 1 if transB else 0
+        int M = A.shape[1 - ta]
+        int N = B.shape[tb]
+        int K = A.shape[ta]
+
+    c_sgemm_dot(&A[0, 0], &B[0, 0], M, N, K, ta, tb, &C[0, 0])
diff --git a/mlprodict/testing/einsum_bench.py b/mlprodict/testing/einsum_bench.py
index 53997f8e9..3095a175b 100644
--- a/mlprodict/testing/einsum_bench.py
+++ b/mlprodict/testing/einsum_bench.py
@@ -127,7 +127,8 @@ def einsum_benchmark(equation="abc,cd->abd", shape=30, perm=False,
             else:
                 onx = seq.to_onnx('Y', *["X%d" % i for i in range(len(inputs))],
                                   opset=opset)
-            sess = InferenceSession(onx.SerializeToString())  # pylint: disable=W0612
+            sess = InferenceSession(
+                onx.SerializeToString())  # pylint: disable=W0612
             fct = lambda *x, se=sess: se.run(
                 None, {"X%d" % i: v for i, v in enumerate(x)})
         elif rt == 'python':
diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py
index 4a5ac2c9e..10caec718 100644
--- a/mlprodict/testing/einsum_impl_classes.py
+++ b/mlprodict/testing/einsum_impl_classes.py
@@ -9,6 +9,7 @@
 from ..tools.onnx2py_helper import guess_proto_dtype
 from ..tools.asv_options_helper import (
     get_opset_number_from_onnx, get_ir_version_from_onnx)
+from .blas_lapack import gemm_dot
 from .einsum_impl_ext import (
     numpy_extended_dot, numpy_diagonal,
     _numpy_extended_dot_equation,
@@ -44,6 +45,10 @@ class EinsumSubOp:
     Operator suffixed by `_mm` (*transpose_mm*, *reduce_sum_mm*)
     are equivalent to the same operator without the suffix
     but takes two inputs and only changes the first one.
+
+    Attributes `_info` summarizes the known information
+    about dimensions. Many of them are empty because inserted.
+    Value `1` means it was the case, `2` means it is a plain dimension.
     """
     _allowed = {'expand_dims', 'transpose', 'reduce_sum', 'matmul', 'id',
                 'squeeze', 'diagonal', 'mul', 'batch_dot',
@@ -510,7 +515,17 @@ def _apply_batch_dot(self, data, verbose=False, **kwargs):
                 self.name, m2.shape, (dim0b, dimb, dim2)))
         m1sh = m1.reshape((dim0, dimb, dim1))
         m2sh = m2.reshape((dim0b, dimb, dim2))
-        dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1))
+
+        batch_kind = self.get_dot_kind()
+        if batch_kind in ('11', 'N1', 'N1'):
+            m1sh = m1sh.reshape((-1, m1sh.shape[-1]))
+            m2sh = m2sh.reshape((-1, m2sh.shape[-1]))
+            if verbose:
+                print("- %s, use gemm with shape %r, %r" % (
+                    self.name, m1sh.shape, m2sh.shape))
+            dot = gemm_dot(m1sh, m2sh, False, True)
+        else:
+            dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1))
 
         # new shape
         new_shape = ([max(m1.shape[i], m2.shape[i]) for i in batch_axes] +
@@ -806,29 +821,55 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs):  # pylint:
             yield helper.make_node(
                 'ReduceProd', [name_dim2g], [name_dim2], keepdims=1)
 
-        # *shape1, *shape2
-        name_agg_shape1 = root + "_resh1"
-        name_agg_shape2 = root + "_resh2"
-        yield helper.make_node(
-            'Concat', concat_left, [name_agg_shape1], axis=0)
-        yield helper.make_node(
-            'Concat', concat_right, [name_agg_shape2], axis=0)
+        batch_kind = self.get_dot_kind()
+        if batch_kind in ('11', 'N1', 'N1'):
+            # *shape1, *shape2
+            name_minus_one = root + "__01"
+            yield numpy_helper.from_array(
+                numpy.array([-1], dtype=numpy.int64), name=name_minus_one)
+            name_agg_shape1_2 = root + "_resh1_%s" % batch_kind
+            name_agg_shape2_2 = root + "_resh2_%s" % batch_kind
+            yield helper.make_node(
+                'Concat', [name_minus_one, name_dim1], [name_agg_shape1_2], axis=0)
+            yield helper.make_node(
+                'Concat', [name_minus_one, name_dim2], [name_agg_shape2_2], axis=0)
 
-        # m1sh = m1.reshape((dim0, dimb, dim1))
-        # m2sh = m2.reshape((dim0b, dimb, dim2))
-        name_agg1 = root + "_aresh1"
-        name_agg2 = root + "_aresh2"
-        yield helper.make_node('Reshape', [name1, name_agg_shape1], [name_agg1])
-        yield helper.make_node('Reshape', [name2, name_agg_shape2], [name_agg2])
+            # m1sh = m1.reshape((-1, dim1))
+            # m2sh = m2.reshape((-1, dim2))
+            name_agg1_2 = root + "_aresh1"
+            name_agg2_2 = root + "_aresh2"
+            yield helper.make_node('Reshape', [name1, name_agg_shape1_2], [name_agg1_2])
+            yield helper.make_node('Reshape', [name2, name_agg_shape2_2], [name_agg2_2])
 
-        # dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1))
-        name_agg2_tr = root + "_aresh2_tr"
-        yield helper.make_node(
-            'Transpose', [name_agg2], [name_agg2_tr], perm=[0, 2, 1])
+            # dot = gemm(m1sh, m2sh, False, True)
+            name_dot = root + "_gemm"
+            yield helper.make_node(
+                'Gemm', [name_agg1_2, name_agg2_2], [name_dot],
+                alpha=1., beta=0., transA=0, transB=1)
+        else:
+            # *shape1, *shape2
+            name_agg_shape1 = root + "_resh1"
+            name_agg_shape2 = root + "_resh2"
+            yield helper.make_node(
+                'Concat', concat_left, [name_agg_shape1], axis=0)
+            yield helper.make_node(
+                'Concat', concat_right, [name_agg_shape2], axis=0)
 
-        name_dot = root + "_dot"
-        yield helper.make_node(
-            'MatMul', [name_agg1, name_agg2_tr], [name_dot])
+            # m1sh = m1.reshape((dim0, dimb, dim1))
+            # m2sh = m2.reshape((dim0b, dimb, dim2))
+            name_agg1 = root + "_aresh1"
+            name_agg2 = root + "_aresh2"
+            yield helper.make_node('Reshape', [name1, name_agg_shape1], [name_agg1])
+            yield helper.make_node('Reshape', [name2, name_agg_shape2], [name_agg2])
+
+            # dot = m1sh @ numpy.transpose(m2sh, (0, 2, 1))
+            name_agg2_tr = root + "_aresh2_tr"
+            yield helper.make_node(
+                'Transpose', [name_agg2], [name_agg2_tr], perm=[0, 2, 1])
+
+            name_dot = root + "_dot"
+            yield helper.make_node(
+                'MatMul', [name_agg1, name_agg2_tr], [name_dot])
 
         # new_shape = ([max(m1.shape[i], m2.shape[i]) for i in batch_axes] +
         #      [m1.shape[i] for i in left if i not in batch_axes] +
@@ -921,6 +962,31 @@ def to_onnx(self, names, opset=None, verbose=False, **kwargs):
                       (node.name, self.name, id(self)))
             yield node
 
+    def get_dot_kind(self):
+        """
+        Every matrix multiplication can be either:
+        * a simple multiplication (`M`) (undetected)
+        * a 2D matrix multiplication (`11`)
+        * a broadcasted matrix multiplication (`N1` or `1N`)
+        * a batch matrix multiplication (`NN`)
+
+        This method returns which kind it is.
+        """
+        batch_axes = self.kwargs['batch_axes']
+        # keep_axes = self.kwargs['keep_axes']
+        # sum_axes = self.kwargs['sum_axes']
+        # left = self.kwargs['left']
+        # right = self.kwargs['right']
+        info = self._info
+        row_left = info['i_row']
+        row_right = info['i_row2']
+
+        batch_left = [row_left[k] for k in batch_axes]
+        batch_right = [row_right[k] for k in batch_axes]
+        n_left = len(batch_left) > 0 and max(batch_left) == 2
+        n_right = len(batch_right) > 0 and max(batch_right) == 2
+        return "%s%s" % ('N' if n_left else '1', 'N' if n_right else '1')
+
 
 class GraphEinsumSubOp:
     """
diff --git a/setup.py b/setup.py
index 25b229d2f..7cf763c9c 100644
--- a/setup.py
+++ b/setup.py
@@ -296,14 +296,21 @@ def get_extensions():
         define_macros=define_macros,
         language='c++')
 
-    cython_extensions = ["direct_blas_lapack"]
-    ext_blas = Extension("mlprodict.testing.direct_blas_lapack",
+    cython_ext = [
+        Extension("mlprodict.testing.direct_blas_lapack",
                   ['mlprodict/testing/direct_blas_lapack.pyx'],
                   include_dirs=[numpy.get_include()],
                   language='c')
+    ]
+
+    from Cython.Build import cythonize
+    opts = dict(boundscheck=False, cdivision=True,
+                wraparound=False, language_level=3,
+                cdivision_warnings=False, embedsignature=True,
+                initializedcheck=False)
+    cy_ext_blas = cythonize(cython_ext, compiler_directives=opts)
 
     ext_modules = [
-        ext_blas,
         ext_conv,
         ext_conv_transpose,
         ext_experimental_c,
@@ -318,6 +325,7 @@ def get_extensions():
         ext_tree_ensemble_regressor_p,
         op_onnx_numpy,
     ]
+    ext_modules.extend(cy_ext_blas)
     return ext_modules
 
 

From c7c5c4b2492befb1e69ae305b062d6993d9019df Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Wed, 5 May 2021 14:53:39 +0200
Subject: [PATCH 3/7] refactoring

---
 .../test_onnx_grammar_bug.py                    |  2 +-
 .../test_onnx_grammar_specific.py               |  7 ++++---
 .../test_onnx_grammar_translate.py              |  5 +++--
 .../test_sklearn_helper.py                      |  4 ++--
 _unittests/ut_onnxrt/test_onnx_helper.py        |  2 +-
 mlprodict/onnx_tools/__init__.py                |  5 +++++
 .../{ => onnx_tools}/onnx_grammar/__init__.py   |  0
 .../onnx_grammar/node_visitor_translator.py     |  0
 .../onnx_grammar/onnx_translation.py            |  0
 .../onnx_grammar/onnx_translator.py             |  0
 .../{onnxrt => onnx_tools}/optim/__init__.py    |  0
 .../optim/_main_onnx_optim.py                   |  0
 .../optim/_onnx_optimisation_common.py          |  0
 .../optim/graph_schema_helper.py                |  0
 .../{onnxrt => onnx_tools}/optim/onnx_helper.py |  0
 .../optim/onnx_optimisation.py                  |  0
 .../optim/onnx_optimisation_identity.py         |  0
 .../optim/onnx_optimisation_redundant.py        |  0
 .../optim/onnx_optimisation_unused.py           |  0
 .../optim/sklearn_helper.py                     |  0
 mlprodict/onnxrt/onnx_inference.py              |  2 +-
 mlprodict/testing/blas_lapack.py                |  2 +-
 mlprodict/testing/einsum_impl_classes.py        | 17 +++++++++++------
 23 files changed, 29 insertions(+), 17 deletions(-)
 rename _unittests/{ut_onnx_grammar => ut_onnx_tools}/test_onnx_grammar_bug.py (92%)
 rename _unittests/{ut_onnx_grammar => ut_onnx_tools}/test_onnx_grammar_specific.py (97%)
 rename _unittests/{ut_onnx_grammar => ut_onnx_tools}/test_onnx_grammar_translate.py (98%)
 rename _unittests/{ut_onnxrt => ut_onnx_tools}/test_sklearn_helper.py (98%)
 create mode 100644 mlprodict/onnx_tools/__init__.py
 rename mlprodict/{ => onnx_tools}/onnx_grammar/__init__.py (100%)
 rename mlprodict/{ => onnx_tools}/onnx_grammar/node_visitor_translator.py (100%)
 rename mlprodict/{ => onnx_tools}/onnx_grammar/onnx_translation.py (100%)
 rename mlprodict/{ => onnx_tools}/onnx_grammar/onnx_translator.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/__init__.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/_main_onnx_optim.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/_onnx_optimisation_common.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/graph_schema_helper.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/onnx_helper.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/onnx_optimisation.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/onnx_optimisation_identity.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/onnx_optimisation_redundant.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/onnx_optimisation_unused.py (100%)
 rename mlprodict/{onnxrt => onnx_tools}/optim/sklearn_helper.py (100%)

diff --git a/_unittests/ut_onnx_grammar/test_onnx_grammar_bug.py b/_unittests/ut_onnx_tools/test_onnx_grammar_bug.py
similarity index 92%
rename from _unittests/ut_onnx_grammar/test_onnx_grammar_bug.py
rename to _unittests/ut_onnx_tools/test_onnx_grammar_bug.py
index c28b09da0..3fc8c9b9d 100644
--- a/_unittests/ut_onnx_grammar/test_onnx_grammar_bug.py
+++ b/_unittests/ut_onnx_tools/test_onnx_grammar_bug.py
@@ -6,7 +6,7 @@
 import inspect
 from textwrap import dedent
 from pyquickhelper.pycode import ExtTestCase
-from mlprodict.onnx_grammar import CodeNodeVisitor
+from mlprodict.onnx_tools.onnx_grammar import CodeNodeVisitor
 
 
 class TestOnnxGrammarBug(ExtTestCase):
diff --git a/_unittests/ut_onnx_grammar/test_onnx_grammar_specific.py b/_unittests/ut_onnx_tools/test_onnx_grammar_specific.py
similarity index 97%
rename from _unittests/ut_onnx_grammar/test_onnx_grammar_specific.py
rename to _unittests/ut_onnx_tools/test_onnx_grammar_specific.py
index 5295e6557..8c464701a 100644
--- a/_unittests/ut_onnx_grammar/test_onnx_grammar_specific.py
+++ b/_unittests/ut_onnx_tools/test_onnx_grammar_specific.py
@@ -7,10 +7,11 @@
 from sklearn.gaussian_process.kernels import ExpSineSquared, DotProduct, RationalQuadratic
 from skl2onnx import __version__ as skl2onnx_version
 from skl2onnx.algebra.onnx_ops import OnnxIdentity  # pylint: disable=E0611
-from mlprodict.onnx_grammar import translate_fct2onnx
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnx_grammar.onnx_translation import get_default_context, get_default_context_cpl
-from mlprodict.onnx_grammar.onnx_translation import (
+from mlprodict.onnx_tools.onnx_grammar import translate_fct2onnx
+from mlprodict.onnx_tools.onnx_grammar.onnx_translation import (
+    get_default_context, get_default_context_cpl)
+from mlprodict.onnx_tools.onnx_grammar.onnx_translation import (
     py_make_float_array, py_pow, squareform_pdist, py_mul, py_opp)
 from mlprodict.tools import get_opset_number_from_onnx
 
diff --git a/_unittests/ut_onnx_grammar/test_onnx_grammar_translate.py b/_unittests/ut_onnx_tools/test_onnx_grammar_translate.py
similarity index 98%
rename from _unittests/ut_onnx_grammar/test_onnx_grammar_translate.py
rename to _unittests/ut_onnx_tools/test_onnx_grammar_translate.py
index e66e51297..c7d4ce25d 100644
--- a/_unittests/ut_onnx_grammar/test_onnx_grammar_translate.py
+++ b/_unittests/ut_onnx_tools/test_onnx_grammar_translate.py
@@ -7,8 +7,9 @@
 from textwrap import dedent
 import numpy
 from pyquickhelper.pycode import ExtTestCase
-from mlprodict.onnx_grammar import CodeNodeVisitor, translate_fct2onnx
-from mlprodict.onnx_grammar.onnx_translation import py_mul
+from mlprodict.onnx_tools.onnx_grammar import (
+    CodeNodeVisitor, translate_fct2onnx)
+from mlprodict.onnx_tools.onnx_grammar.onnx_translation import py_mul
 from mlprodict.onnxrt import OnnxInference
 from mlprodict.tools import get_opset_number_from_onnx
 
diff --git a/_unittests/ut_onnxrt/test_sklearn_helper.py b/_unittests/ut_onnx_tools/test_sklearn_helper.py
similarity index 98%
rename from _unittests/ut_onnxrt/test_sklearn_helper.py
rename to _unittests/ut_onnx_tools/test_sklearn_helper.py
index 20b87aa89..595f71885 100644
--- a/_unittests/ut_onnxrt/test_sklearn_helper.py
+++ b/_unittests/ut_onnx_tools/test_sklearn_helper.py
@@ -21,9 +21,9 @@
 from skl2onnx.algebra.onnx_ops import (  # pylint: disable=E0611
     OnnxIdentity, OnnxAdd)
 from skl2onnx.common.data_types import FloatTensorType
-from mlprodict.onnxrt.optim.sklearn_helper import (
+from mlprodict.onnx_tools.optim.sklearn_helper import (
     enumerate_pipeline_models, inspect_sklearn_model, set_n_jobs)
-from mlprodict.onnxrt.optim.onnx_helper import onnx_statistics
+from mlprodict.onnx_tools.optim.onnx_helper import onnx_statistics
 from mlprodict.onnx_conv import to_onnx
 from mlprodict.tools import get_opset_number_from_onnx
 
diff --git a/_unittests/ut_onnxrt/test_onnx_helper.py b/_unittests/ut_onnxrt/test_onnx_helper.py
index 7a4f17e3e..4eb0109a6 100644
--- a/_unittests/ut_onnxrt/test_onnx_helper.py
+++ b/_unittests/ut_onnxrt/test_onnx_helper.py
@@ -9,7 +9,7 @@
 from sklearn.datasets import load_iris
 from sklearn.cluster import KMeans
 from mlprodict.onnx_conv import to_onnx
-from mlprodict.onnxrt.optim.onnx_helper import change_input_first_dimension
+from mlprodict.onnx_tools.optim.onnx_helper import change_input_first_dimension
 from mlprodict.tools.onnx2py_helper import (
     to_bytes, from_bytes, numpy_max, numpy_min, _type_to_string,
     _numpy_array)
diff --git a/mlprodict/onnx_tools/__init__.py b/mlprodict/onnx_tools/__init__.py
new file mode 100644
index 000000000..cc4b0e022
--- /dev/null
+++ b/mlprodict/onnx_tools/__init__.py
@@ -0,0 +1,5 @@
+# -*- encoding: utf-8 -*-
+"""
+@file
+@brief Shortcut to *onnx_tools*.
+"""
diff --git a/mlprodict/onnx_grammar/__init__.py b/mlprodict/onnx_tools/onnx_grammar/__init__.py
similarity index 100%
rename from mlprodict/onnx_grammar/__init__.py
rename to mlprodict/onnx_tools/onnx_grammar/__init__.py
diff --git a/mlprodict/onnx_grammar/node_visitor_translator.py b/mlprodict/onnx_tools/onnx_grammar/node_visitor_translator.py
similarity index 100%
rename from mlprodict/onnx_grammar/node_visitor_translator.py
rename to mlprodict/onnx_tools/onnx_grammar/node_visitor_translator.py
diff --git a/mlprodict/onnx_grammar/onnx_translation.py b/mlprodict/onnx_tools/onnx_grammar/onnx_translation.py
similarity index 100%
rename from mlprodict/onnx_grammar/onnx_translation.py
rename to mlprodict/onnx_tools/onnx_grammar/onnx_translation.py
diff --git a/mlprodict/onnx_grammar/onnx_translator.py b/mlprodict/onnx_tools/onnx_grammar/onnx_translator.py
similarity index 100%
rename from mlprodict/onnx_grammar/onnx_translator.py
rename to mlprodict/onnx_tools/onnx_grammar/onnx_translator.py
diff --git a/mlprodict/onnxrt/optim/__init__.py b/mlprodict/onnx_tools/optim/__init__.py
similarity index 100%
rename from mlprodict/onnxrt/optim/__init__.py
rename to mlprodict/onnx_tools/optim/__init__.py
diff --git a/mlprodict/onnxrt/optim/_main_onnx_optim.py b/mlprodict/onnx_tools/optim/_main_onnx_optim.py
similarity index 100%
rename from mlprodict/onnxrt/optim/_main_onnx_optim.py
rename to mlprodict/onnx_tools/optim/_main_onnx_optim.py
diff --git a/mlprodict/onnxrt/optim/_onnx_optimisation_common.py b/mlprodict/onnx_tools/optim/_onnx_optimisation_common.py
similarity index 100%
rename from mlprodict/onnxrt/optim/_onnx_optimisation_common.py
rename to mlprodict/onnx_tools/optim/_onnx_optimisation_common.py
diff --git a/mlprodict/onnxrt/optim/graph_schema_helper.py b/mlprodict/onnx_tools/optim/graph_schema_helper.py
similarity index 100%
rename from mlprodict/onnxrt/optim/graph_schema_helper.py
rename to mlprodict/onnx_tools/optim/graph_schema_helper.py
diff --git a/mlprodict/onnxrt/optim/onnx_helper.py b/mlprodict/onnx_tools/optim/onnx_helper.py
similarity index 100%
rename from mlprodict/onnxrt/optim/onnx_helper.py
rename to mlprodict/onnx_tools/optim/onnx_helper.py
diff --git a/mlprodict/onnxrt/optim/onnx_optimisation.py b/mlprodict/onnx_tools/optim/onnx_optimisation.py
similarity index 100%
rename from mlprodict/onnxrt/optim/onnx_optimisation.py
rename to mlprodict/onnx_tools/optim/onnx_optimisation.py
diff --git a/mlprodict/onnxrt/optim/onnx_optimisation_identity.py b/mlprodict/onnx_tools/optim/onnx_optimisation_identity.py
similarity index 100%
rename from mlprodict/onnxrt/optim/onnx_optimisation_identity.py
rename to mlprodict/onnx_tools/optim/onnx_optimisation_identity.py
diff --git a/mlprodict/onnxrt/optim/onnx_optimisation_redundant.py b/mlprodict/onnx_tools/optim/onnx_optimisation_redundant.py
similarity index 100%
rename from mlprodict/onnxrt/optim/onnx_optimisation_redundant.py
rename to mlprodict/onnx_tools/optim/onnx_optimisation_redundant.py
diff --git a/mlprodict/onnxrt/optim/onnx_optimisation_unused.py b/mlprodict/onnx_tools/optim/onnx_optimisation_unused.py
similarity index 100%
rename from mlprodict/onnxrt/optim/onnx_optimisation_unused.py
rename to mlprodict/onnx_tools/optim/onnx_optimisation_unused.py
diff --git a/mlprodict/onnxrt/optim/sklearn_helper.py b/mlprodict/onnx_tools/optim/sklearn_helper.py
similarity index 100%
rename from mlprodict/onnxrt/optim/sklearn_helper.py
rename to mlprodict/onnx_tools/optim/sklearn_helper.py
diff --git a/mlprodict/onnxrt/onnx_inference.py b/mlprodict/onnxrt/onnx_inference.py
index 405dbd946..6f33b2ee8 100644
--- a/mlprodict/onnxrt/onnx_inference.py
+++ b/mlprodict/onnxrt/onnx_inference.py
@@ -18,9 +18,9 @@
 from ..tools.onnx2py_helper import _var_as_dict, numpy_min, numpy_max
 from ..tools.onnx_manipulations import (
     select_model_inputs_outputs, enumerate_model_node_outputs)
+from ..onnx_tools.optim import onnx_remove_node_unused
 from .onnx_inference_node import OnnxInferenceNode
 from .onnx_inference_exports import OnnxInferenceExport
-from .optim import onnx_remove_node_unused
 from .shape_object import ShapeObject
 
 
diff --git a/mlprodict/testing/blas_lapack.py b/mlprodict/testing/blas_lapack.py
index af3c4f47c..9fc5e1ec0 100644
--- a/mlprodict/testing/blas_lapack.py
+++ b/mlprodict/testing/blas_lapack.py
@@ -4,7 +4,7 @@
 """
 import numpy
 from scipy.linalg.blas import sgemm, dgemm  # pylint: disable=E0611
-from .direct_blas_lapack import (  # pylint: disable=E0401
+from .direct_blas_lapack import (  # pylint: disable=E0401,E0611
     dgemm_dot, sgemm_dot)
 
 
diff --git a/mlprodict/testing/einsum_impl_classes.py b/mlprodict/testing/einsum_impl_classes.py
index 10caec718..11d2804e6 100644
--- a/mlprodict/testing/einsum_impl_classes.py
+++ b/mlprodict/testing/einsum_impl_classes.py
@@ -697,13 +697,12 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs):  # pylint:
         right = self.kwargs['right']
         root = self._onnx_name()
 
-        name_one = root + "_1"
-        name_zero = root + "_0"
-        yield numpy_helper.from_array(
-            numpy.array([1], dtype=numpy.int64), name=name_one)
-        yield numpy_helper.from_array(
-            numpy.array([0], dtype=numpy.int64), name=name_zero)
+        def return_name_one():
+            name_one = root + "_1"
+            return name_one, numpy_helper.from_array(
+                numpy.array([1], dtype=numpy.int64), name=name_one)
 
+        name_one = None
         name_shape1 = root + "_shape1"
         name_shape2 = root + "_shape2"
         concat_left = []
@@ -750,6 +749,9 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs):  # pylint:
             yield helper.make_node(
                 'Gather', [name_shape2, name_batch_axes], [name_dim0bg])
         else:
+            if name_one is None:
+                name_one, cst_init = return_name_one()
+                yield cst_init
             name_dim0 = name_one
             name_dim0b = name_one
             concat_left.append(name_dim0)
@@ -790,6 +792,9 @@ def _to_onnx_batch_dot(self, names, opset, verbose=False, **kwargs):  # pylint:
         # dim2 = int(numpy.prod([m2.shape[i] for i in sum_axes]))
 
         if len(sum_axes) == 0:
+            if name_one is None:
+                name_one, cst_init = return_name_one()
+                yield cst_init
             name_dim1 = name_one
             name_dim2 = name_one
             concat_left.append(name_dim1)

From 5a355a063760ab4c7d8caa10570d09ee6a45a7c6 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Wed, 5 May 2021 15:03:02 +0200
Subject: [PATCH 4/7] renamings

---
 _doc/sphinxdoc/source/api/onnxrt.rst          |  8 ++--
 _doc/sphinxdoc/source/api/tools.rst           |  4 +-
 ...test_run_notebooks_einsum_decomposition.py | 42 +++++++++++++++++++
 .../test_onnxrt_simple_gaussian_process.py    |  2 +-
 .../ut_onnxrt/test_onnxrt_switch_types.py     |  2 +-
 .../ut_onnxrt/test_optim_onnx_identity.py     |  4 +-
 .../ut_onnxrt/test_optim_onnx_redundant.py    |  4 +-
 .../ut_onnxrt/test_optim_onnx_unused.py       |  4 +-
 _unittests/ut_tools/test_onnx_maniplations.py |  4 +-
 mlprodict/asv_benchmark/_create_asv_helper.py |  6 +--
 mlprodict/asv_benchmark/common_asv_skl.py     |  2 +-
 mlprodict/cli/convert_validate.py             |  2 +-
 mlprodict/cli/optimize.py                     |  4 +-
 mlprodict/onnx_tools/optim/__init__.py        |  2 +-
 mlprodict/onnx_tools/optim/onnx_helper.py     |  2 +-
 mlprodict/onnx_tools/optim/sklearn_helper.py  |  6 +--
 mlprodict/sklapi/onnx_transformer.py          |  2 +-
 mlprodict/tools/model_info.py                 |  2 +-
 18 files changed, 72 insertions(+), 30 deletions(-)
 create mode 100644 _unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py

diff --git a/_doc/sphinxdoc/source/api/onnxrt.rst b/_doc/sphinxdoc/source/api/onnxrt.rst
index 2968faf46..6fdd5ee5b 100644
--- a/_doc/sphinxdoc/source/api/onnxrt.rst
+++ b/_doc/sphinxdoc/source/api/onnxrt.rst
@@ -122,13 +122,13 @@ The following functions reduce the number of ONNX operators in a graph
 while keeping the same results. The optimized graph
 is left unchanged.
 
-.. autosignature:: mlprodict.onnxrt.optim.onnx_optimisation.onnx_remove_node
+.. autosignature:: mlprodict.onnx_tools.optim.onnx_optimisation.onnx_remove_node
 
-.. autosignature:: mlprodict.onnxrt.optim.onnx_optimisation_identity.onnx_remove_node_identity
+.. autosignature:: mlprodict.onnx_tools.optim.onnx_optimisation_identity.onnx_remove_node_identity
 
-.. autosignature:: mlprodict.onnxrt.optim.onnx_optimisation_redundant.onnx_remove_node_redundant
+.. autosignature:: mlprodict.onnx_tools.optim.onnx_optimisation_redundant.onnx_remove_node_redundant
 
-.. autosignature:: mlprodict.onnxrt.optim.onnx_remove_unused.onnx_remove_node_unused
+.. autosignature:: mlprodict.onnx_tools.optim.onnx_remove_unused.onnx_remove_node_unused
 
 Shapes
 ++++++
diff --git a/_doc/sphinxdoc/source/api/tools.rst b/_doc/sphinxdoc/source/api/tools.rst
index 855216ecb..99218273a 100644
--- a/_doc/sphinxdoc/source/api/tools.rst
+++ b/_doc/sphinxdoc/source/api/tools.rst
@@ -23,9 +23,9 @@ Functions to help understand models.
 
 .. autosignature:: mlprodict.onnxrt.model_checker.onnx_shaker
 
-.. autosignature:: mlprodict.onnxrt.optimisation._main_onnx_optim.onnx_optimisations
+.. autosignature:: mlprodict.onnx_tools.optimisation._main_onnx_optim.onnx_optimisations
 
-.. autosignature:: mlprodict.onnxrt.optim.onnx_statistics
+.. autosignature:: mlprodict.onnx_tools.optim.onnx_statistics
 
 .. autosignature:: mlprodict.tools.onnx_manipulations.select_model_inputs_outputs
 
diff --git a/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py b/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py
new file mode 100644
index 000000000..73df7262a
--- /dev/null
+++ b/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py
@@ -0,0 +1,42 @@
+# -*- coding: utf-8 -*-
+"""
+@brief      test log(time=30s)
+"""
+import os
+import unittest
+from onnxruntime import __version__ as ort_version
+from sklearn.exceptions import ConvergenceWarning
+try:
+    from sklearn.utils._testing import ignore_warnings
+except ImportError:
+    from sklearn.utils.testing import ignore_warnings
+from pyquickhelper.loghelper import fLOG
+from pyquickhelper.texthelper.version_helper import compare_module_version
+from pyquickhelper.ipythonhelper import test_notebook_execution_coverage
+from pyquickhelper.pycode import (
+    add_missing_development_version, ExtTestCase)
+from skl2onnx import __version__ as skl2onnx_version
+import mlprodict
+
+
+class TestNotebookOnnxDecomposition(ExtTestCase):
+
+    def setUp(self):
+        add_missing_development_version(["jyquickhelper"], __file__, hide=True)
+
+    @ignore_warnings(category=(UserWarning, ConvergenceWarning, RuntimeWarning))
+    def test_notebook_numpy_onnx(self):
+        fLOG(
+            __file__,
+            self._testMethodName,
+            OutputPrint=__name__ == "__main__")
+
+        self.assertNotEmpty(mlprodict is not None)
+        folder = os.path.join(os.path.dirname(__file__),
+                              "..", "..", "_doc", "notebooks")
+        test_notebook_execution_coverage(__file__, "einsum_decomposition", folder,
+                                         this_module_name="mlprodict", fLOG=fLOG)
+
+
+if __name__ == "__main__":
+    unittest.main()
diff --git a/_unittests/ut_onnxrt/test_onnxrt_simple_gaussian_process.py b/_unittests/ut_onnxrt/test_onnxrt_simple_gaussian_process.py
index a5f59bc07..8530b3cda 100644
--- a/_unittests/ut_onnxrt/test_onnxrt_simple_gaussian_process.py
+++ b/_unittests/ut_onnxrt/test_onnxrt_simple_gaussian_process.py
@@ -11,7 +11,7 @@
 from skl2onnx import __version__ as skl2onnx_version
 from mlprodict.onnx_conv import to_onnx
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnxrt.optim import onnx_optimisations
+from mlprodict.onnx_tools.optim import onnx_optimisations
 
 
 class TestOnnxrtSimpleGaussianProcess(ExtTestCase):
diff --git a/_unittests/ut_onnxrt/test_onnxrt_switch_types.py b/_unittests/ut_onnxrt/test_onnxrt_switch_types.py
index 3954a8823..63ae5257c 100644
--- a/_unittests/ut_onnxrt/test_onnxrt_switch_types.py
+++ b/_unittests/ut_onnxrt/test_onnxrt_switch_types.py
@@ -16,7 +16,7 @@
 from skl2onnx.algebra.onnx_ops import OnnxAdd  # pylint: disable=E0611
 from mlprodict.onnx_conv import to_onnx
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnxrt.optim.sklearn_helper import (
+from mlprodict.onnx_tools.optim.sklearn_helper import (
     enumerate_fitted_arrays, pairwise_array_distances)
 from mlprodict.tools import get_opset_number_from_onnx
 
diff --git a/_unittests/ut_onnxrt/test_optim_onnx_identity.py b/_unittests/ut_onnxrt/test_optim_onnx_identity.py
index e75344213..02fdbb66c 100644
--- a/_unittests/ut_onnxrt/test_optim_onnx_identity.py
+++ b/_unittests/ut_onnxrt/test_optim_onnx_identity.py
@@ -13,9 +13,9 @@
 from skl2onnx.common.data_types import FloatTensorType
 from skl2onnx.algebra.complex_functions import onnx_cdist
 from mlprodict.onnx_conv import to_onnx
-from mlprodict.onnxrt.optim.onnx_helper import onnx_statistics
+from mlprodict.onnx_tools.optim.onnx_helper import onnx_statistics
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnxrt.optim import onnx_remove_node_identity
+from mlprodict.onnx_tools.optim import onnx_remove_node_identity
 from mlprodict.tools import get_opset_number_from_onnx
 
 
diff --git a/_unittests/ut_onnxrt/test_optim_onnx_redundant.py b/_unittests/ut_onnxrt/test_optim_onnx_redundant.py
index 1ebabbc1b..e45a8e905 100644
--- a/_unittests/ut_onnxrt/test_optim_onnx_redundant.py
+++ b/_unittests/ut_onnxrt/test_optim_onnx_redundant.py
@@ -8,9 +8,9 @@
     OnnxAdd, OnnxMul, OnnxSub, OnnxIdentity
 )
 from skl2onnx.common.data_types import FloatTensorType
-from mlprodict.onnxrt.optim.onnx_helper import onnx_statistics
+from mlprodict.onnx_tools.optim.onnx_helper import onnx_statistics
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnxrt.optim import (
+from mlprodict.onnx_tools.optim import (
     onnx_remove_node_redundant, onnx_remove_node, onnx_optimisations)
 from mlprodict.tools import get_opset_number_from_onnx
 
diff --git a/_unittests/ut_onnxrt/test_optim_onnx_unused.py b/_unittests/ut_onnxrt/test_optim_onnx_unused.py
index adf38b160..ffeb79756 100644
--- a/_unittests/ut_onnxrt/test_optim_onnx_unused.py
+++ b/_unittests/ut_onnxrt/test_optim_onnx_unused.py
@@ -6,9 +6,9 @@
 from pyquickhelper.pycode import ExtTestCase
 from skl2onnx.algebra.onnx_ops import (  # pylint: disable=E0611
     OnnxAdd, OnnxMul, OnnxSub)
-from mlprodict.onnxrt.optim.onnx_helper import onnx_statistics
+from mlprodict.onnx_tools.optim.onnx_helper import onnx_statistics
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnxrt.optim import onnx_remove_node_unused
+from mlprodict.onnx_tools.optim import onnx_remove_node_unused
 from mlprodict.tools.onnx_manipulations import (
     select_model_inputs_outputs)
 from mlprodict.tools import get_opset_number_from_onnx
diff --git a/_unittests/ut_tools/test_onnx_maniplations.py b/_unittests/ut_tools/test_onnx_maniplations.py
index 2e6bb4d3d..01f2189be 100644
--- a/_unittests/ut_tools/test_onnx_maniplations.py
+++ b/_unittests/ut_tools/test_onnx_maniplations.py
@@ -6,9 +6,9 @@
 from pyquickhelper.pycode import ExtTestCase
 from skl2onnx.algebra.onnx_ops import (  # pylint: disable=E0611
     OnnxAdd, OnnxMul, OnnxSub)
-from mlprodict.onnxrt.optim.onnx_helper import onnx_statistics
+from mlprodict.onnx_tools.optim.onnx_helper import onnx_statistics
 from mlprodict.onnxrt import OnnxInference
-from mlprodict.onnxrt.optim import onnx_remove_node_unused
+from mlprodict.onnx_tools.optim import onnx_remove_node_unused
 from mlprodict.tools.onnx_manipulations import (
     select_model_inputs_outputs)
 from mlprodict.tools import get_opset_number_from_onnx
diff --git a/mlprodict/asv_benchmark/_create_asv_helper.py b/mlprodict/asv_benchmark/_create_asv_helper.py
index 32d81d8fe..d989d0a28 100644
--- a/mlprodict/asv_benchmark/_create_asv_helper.py
+++ b/mlprodict/asv_benchmark/_create_asv_helper.py
@@ -6,9 +6,9 @@
 import textwrap
 import hashlib
 try:
-    from ..onnxrt.optim.sklearn_helper import set_n_jobs
+    from ..onnx_tools.optim.sklearn_helper import set_n_jobs
 except (ValueError, ImportError):  # pragma: no cover
-    from mlprodict.onnxrt.optim.sklearn_helper import set_n_jobs
+    from mlprodict.onnx_tools.optim.sklearn_helper import set_n_jobs
 
 # exec function does not import models but potentially
 # requires all specific models used to defines scenarios
@@ -405,7 +405,7 @@ def add_model_import_init(
     # additional methods and imports
     if optimisation is not None:
         add_imports.append(
-            'from mlprodict.onnxrt.optim import onnx_optimisations')
+            'from mlprodict.onnx_tools.optim import onnx_optimisations')
         if optimisation == 'onnx':
             add_methods.append(textwrap.dedent('''
                 def _optimize_onnx(self, onx):
diff --git a/mlprodict/asv_benchmark/common_asv_skl.py b/mlprodict/asv_benchmark/common_asv_skl.py
index 9bb2ea2ff..3d43e4f19 100644
--- a/mlprodict/asv_benchmark/common_asv_skl.py
+++ b/mlprodict/asv_benchmark/common_asv_skl.py
@@ -27,7 +27,7 @@
     to_onnx, register_rewritten_operators, register_converters)
 from mlprodict.onnxrt.validate.validate_benchmark import make_n_rows
 from mlprodict.onnxrt.validate.validate_problems import _modify_dimension
-from mlprodict.onnxrt.optim import onnx_statistics
+from mlprodict.onnx_tools.optim import onnx_statistics
 from mlprodict.tools.asv_options_helper import (
     expand_onnx_options, get_opset_number_from_onnx,
     get_ir_version_from_onnx, version2number)
diff --git a/mlprodict/cli/convert_validate.py b/mlprodict/cli/convert_validate.py
index a41a85ca5..2891c7eac 100644
--- a/mlprodict/cli/convert_validate.py
+++ b/mlprodict/cli/convert_validate.py
@@ -10,7 +10,7 @@
 from skl2onnx.common.data_types import FloatTensorType, DoubleTensorType
 from ..onnx_conv import to_onnx
 from ..onnxrt import OnnxInference
-from ..onnxrt.optim import onnx_optimisations
+from ..onnx_tools.optim import onnx_optimisations
 from ..onnxrt.validate.validate_difference import measure_relative_difference
 from ..onnx_conv import guess_schema_from_data, guess_schema_from_model
 
diff --git a/mlprodict/cli/optimize.py b/mlprodict/cli/optimize.py
index 60b65ff06..6cae7206a 100644
--- a/mlprodict/cli/optimize.py
+++ b/mlprodict/cli/optimize.py
@@ -20,7 +20,7 @@ def onnx_stats(name, optim=False):
 
         The command computes statistics on an ONNX model.
     """
-    from ..onnxrt.optim import onnx_statistics
+    from ..onnx_tools.optim import onnx_statistics
     if not os.path.exists(name):
         raise FileNotFoundError(  # pragma: no cover
             "Unable to find file '{}'.".format(name))
@@ -47,7 +47,7 @@ def onnx_optim(name, outfile=None, recursive=True, options=None, verbose=0, fLOG
 
         The command optimises an ONNX model.
     """
-    from ..onnxrt.optim import onnx_statistics, onnx_optimisations
+    from ..onnx_tools.optim import onnx_statistics, onnx_optimisations
     if not os.path.exists(name):
         raise FileNotFoundError(  # pragma: no cover
             "Unable to find file '{}'.".format(name))
diff --git a/mlprodict/onnx_tools/optim/__init__.py b/mlprodict/onnx_tools/optim/__init__.py
index 0be2269c6..67b2a4a80 100644
--- a/mlprodict/onnx_tools/optim/__init__.py
+++ b/mlprodict/onnx_tools/optim/__init__.py
@@ -1,6 +1,6 @@
 """
 @file
-@brief Shortcuts to *onnxrt.optim*.
+@brief Shortcuts to *onnx_tools.optim*.
 """
 from .onnx_helper import onnx_statistics
 from .onnx_optimisation_identity import onnx_remove_node_identity
diff --git a/mlprodict/onnx_tools/optim/onnx_helper.py b/mlprodict/onnx_tools/optim/onnx_helper.py
index f47f5d7da..7cd0cd816 100644
--- a/mlprodict/onnx_tools/optim/onnx_helper.py
+++ b/mlprodict/onnx_tools/optim/onnx_helper.py
@@ -29,7 +29,7 @@ def onnx_statistics(onnx_model, recursive=True, optim=True):
         from sklearn.linear_model import LogisticRegression
         from sklearn.ensemble import RandomForestClassifier
         from sklearn.datasets import load_iris
-        from mlprodict.onnxrt.optim.onnx_helper import onnx_statistics
+        from mlprodict.onnx_tools.optim.onnx_helper import onnx_statistics
         from mlprodict.onnx_conv import to_onnx
 
         iris = load_iris()
diff --git a/mlprodict/onnx_tools/optim/sklearn_helper.py b/mlprodict/onnx_tools/optim/sklearn_helper.py
index ccc3f95a3..8177c7fde 100644
--- a/mlprodict/onnx_tools/optim/sklearn_helper.py
+++ b/mlprodict/onnx_tools/optim/sklearn_helper.py
@@ -31,7 +31,7 @@ def enumerate_pipeline_models(pipe, coor=None, vs=None):
         from sklearn.linear_model import LogisticRegression
         from sklearn.pipeline import make_pipeline
         from sklearn.model_selection import train_test_split
-        from mlprodict.onnxrt.optim.sklearn_helper import enumerate_pipeline_models
+        from mlprodict.onnx_tools.optim.sklearn_helper import enumerate_pipeline_models
 
         iris = load_iris()
         X, y = iris.data, iris.target
@@ -114,7 +114,7 @@ def enumerate_fitted_arrays(model):
         from sklearn.linear_model import LogisticRegression
         from sklearn.pipeline import make_pipeline
         from sklearn.model_selection import train_test_split
-        from mlprodict.onnxrt.optim.sklearn_helper import enumerate_fitted_arrays
+        from mlprodict.onnx_tools.optim.sklearn_helper import enumerate_fitted_arrays
 
         iris = load_iris()
         X, y = iris.data, iris.target
@@ -224,7 +224,7 @@ def inspect_sklearn_model(model, recursive=True):
         from sklearn.ensemble import RandomForestClassifier
         from sklearn.linear_model import LogisticRegression
         from sklearn.datasets import load_iris
-        from mlprodict.onnxrt.optim.sklearn_helper import inspect_sklearn_model
+        from mlprodict.onnx_tools.optim.sklearn_helper import inspect_sklearn_model
 
         iris = load_iris()
         X = iris.data
diff --git a/mlprodict/sklapi/onnx_transformer.py b/mlprodict/sklapi/onnx_transformer.py
index f3b3122b2..cb9ebbecc 100644
--- a/mlprodict/sklapi/onnx_transformer.py
+++ b/mlprodict/sklapi/onnx_transformer.py
@@ -81,7 +81,7 @@ def fit(self, X=None, y=None, **fit_params):
         :param fit_params: additional parameter (unused)
         :return: self
         """
-        from ..onnxrt.optim.onnx_helper import change_input_first_dimension
+        from ..onnx_tools.optim.onnx_helper import change_input_first_dimension
         onx = onnx.load(BytesIO(self.onnx_bytes))
 
         output_names = set(
diff --git a/mlprodict/tools/model_info.py b/mlprodict/tools/model_info.py
index 04cbf7146..169855d34 100644
--- a/mlprodict/tools/model_info.py
+++ b/mlprodict/tools/model_info.py
@@ -192,7 +192,7 @@ def analyze_model(model, simplify=True):
     """
     if hasattr(model, 'SerializeToString'):
         # ONNX model
-        from ..onnxrt.optim.onnx_helper import onnx_statistics
+        from ..onnx_tools.optim.onnx_helper import onnx_statistics
         return onnx_statistics(model)
 
     if isinstance(model, numpy.ndarray):

From ba38c84ff17d9229309b7088be7fe7655ec1fe56 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Wed, 5 May 2021 15:18:55 +0200
Subject: [PATCH 5/7] renamings

---
 mlprodict/onnxrt/doc/doc_write_helper.py | 4 ++--
 mlprodict/onnxrt/onnx_inference.py       | 2 +-
 mlprodict/onnxrt/ops_empty/_op.py        | 2 +-
 mlprodict/onnxrt/ops_onnxruntime/_op.py  | 2 +-
 mlprodict/onnxrt/validate/validate.py    | 6 +++---
 5 files changed, 8 insertions(+), 8 deletions(-)

diff --git a/mlprodict/onnxrt/doc/doc_write_helper.py b/mlprodict/onnxrt/doc/doc_write_helper.py
index 34fa84b7f..82c01485f 100644
--- a/mlprodict/onnxrt/doc/doc_write_helper.py
+++ b/mlprodict/onnxrt/doc/doc_write_helper.py
@@ -14,8 +14,8 @@
 from ...tools.asv_options_helper import get_opset_number_from_onnx
 from ...tools.model_info import analyze_model
 from ..validate.validate import enumerate_validated_operator_opsets, sklearn_operators
-from ..optim.sklearn_helper import inspect_sklearn_model
-from ..optim.onnx_helper import onnx_statistics
+from ...onnx_tools.sklearn_helper import inspect_sklearn_model
+from ...onnx_tools.onnx_helper import onnx_statistics
 from ..onnx_inference import OnnxInference
 from ..validate.validate_summary import _clean_values_optim
 from .doc_helper import visual_rst_template
diff --git a/mlprodict/onnxrt/onnx_inference.py b/mlprodict/onnxrt/onnx_inference.py
index 6f33b2ee8..6a33af0f6 100644
--- a/mlprodict/onnxrt/onnx_inference.py
+++ b/mlprodict/onnxrt/onnx_inference.py
@@ -842,7 +842,7 @@ def switch_initializers_dtype(self, model=None,
         @param      dtype_out   next type
         @return                 done operations
         """
-        from .optim.sklearn_helper import enumerate_fitted_arrays, pairwise_array_distances
+        from ..onnx_tools.optim.sklearn_helper import enumerate_fitted_arrays, pairwise_array_distances
 
         if self.runtime != 'python':  # pragma: no cover
             raise RuntimeError("Initializers can be casted only if the "
diff --git a/mlprodict/onnxrt/ops_empty/_op.py b/mlprodict/onnxrt/ops_empty/_op.py
index 641d2c2df..b7c26245d 100644
--- a/mlprodict/onnxrt/ops_empty/_op.py
+++ b/mlprodict/onnxrt/ops_empty/_op.py
@@ -13,7 +13,7 @@
     # older version of skl2onnx
     alg2 = alg
 from ...tools.onnx2py_helper import guess_proto_dtype
-from ..optim.graph_schema_helper import (
+from ...onnx_tools.graph_schema_helper import (
     get_defined_inputs, get_defined_outputs, proto2vars)
 
 
diff --git a/mlprodict/onnxrt/ops_onnxruntime/_op.py b/mlprodict/onnxrt/ops_onnxruntime/_op.py
index 43423f232..a48a3ca31 100644
--- a/mlprodict/onnxrt/ops_onnxruntime/_op.py
+++ b/mlprodict/onnxrt/ops_onnxruntime/_op.py
@@ -20,7 +20,7 @@
     # older version of skl2onnx
     alg2 = alg
 from ...tools.onnx2py_helper import guess_proto_dtype
-from ..optim.graph_schema_helper import (
+from ...onnx_tools.graph_schema_helper import (
     get_defined_inputs, get_defined_outputs, proto2vars)
 
 
diff --git a/mlprodict/onnxrt/validate/validate.py b/mlprodict/onnxrt/validate/validate.py
index b798ceabc..fec1de4cc 100644
--- a/mlprodict/onnxrt/validate/validate.py
+++ b/mlprodict/onnxrt/validate/validate.py
@@ -18,9 +18,9 @@
 from ...tools.asv_options_helper import (
     get_opset_number_from_onnx, get_ir_version_from_onnx)
 from ..onnx_inference import OnnxInference
-from ..optim.sklearn_helper import inspect_sklearn_model, set_n_jobs
-from ..optim.onnx_helper import onnx_statistics
-from ..optim import onnx_optimisations
+from ...onnx_tools.optim.sklearn_helper import inspect_sklearn_model, set_n_jobs
+from ...onnx_tools.optim.onnx_helper import onnx_statistics
+from ...onnx_tools.optim import onnx_optimisations
 from .validate_problems import find_suitable_problem
 from .validate_scenarios import _extra_parameters
 from .validate_difference import measure_relative_difference

From a2a0e22ff256ff0820710ce27caa44c88d4f4cf3 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Wed, 5 May 2021 15:42:32 +0200
Subject: [PATCH 6/7] renamings

---
 mlprodict/onnxrt/doc/doc_write_helper.py | 4 ++--
 mlprodict/onnxrt/ops_empty/_op.py        | 2 +-
 mlprodict/onnxrt/ops_onnxruntime/_op.py  | 2 +-
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/mlprodict/onnxrt/doc/doc_write_helper.py b/mlprodict/onnxrt/doc/doc_write_helper.py
index 82c01485f..fad22a3c4 100644
--- a/mlprodict/onnxrt/doc/doc_write_helper.py
+++ b/mlprodict/onnxrt/doc/doc_write_helper.py
@@ -14,8 +14,8 @@
 from ...tools.asv_options_helper import get_opset_number_from_onnx
 from ...tools.model_info import analyze_model
 from ..validate.validate import enumerate_validated_operator_opsets, sklearn_operators
-from ...onnx_tools.sklearn_helper import inspect_sklearn_model
-from ...onnx_tools.onnx_helper import onnx_statistics
+from ...onnx_tools.optim.sklearn_helper import inspect_sklearn_model
+from ...onnx_tools.optim.onnx_helper import onnx_statistics
 from ..onnx_inference import OnnxInference
 from ..validate.validate_summary import _clean_values_optim
 from .doc_helper import visual_rst_template
diff --git a/mlprodict/onnxrt/ops_empty/_op.py b/mlprodict/onnxrt/ops_empty/_op.py
index b7c26245d..d95c3810c 100644
--- a/mlprodict/onnxrt/ops_empty/_op.py
+++ b/mlprodict/onnxrt/ops_empty/_op.py
@@ -13,7 +13,7 @@
     # older version of skl2onnx
     alg2 = alg
 from ...tools.onnx2py_helper import guess_proto_dtype
-from ...onnx_tools.graph_schema_helper import (
+from ...onnx_tools.optim.graph_schema_helper import (
     get_defined_inputs, get_defined_outputs, proto2vars)
 
 
diff --git a/mlprodict/onnxrt/ops_onnxruntime/_op.py b/mlprodict/onnxrt/ops_onnxruntime/_op.py
index a48a3ca31..3b7779446 100644
--- a/mlprodict/onnxrt/ops_onnxruntime/_op.py
+++ b/mlprodict/onnxrt/ops_onnxruntime/_op.py
@@ -20,7 +20,7 @@
     # older version of skl2onnx
     alg2 = alg
 from ...tools.onnx2py_helper import guess_proto_dtype
-from ...onnx_tools.graph_schema_helper import (
+from ...onnx_tools.optim.graph_schema_helper import (
     get_defined_inputs, get_defined_outputs, proto2vars)
 
 

From 8cf1320fe00c685eaf07e3208048d4d59a30a170 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?xavier=20dupr=C3=A9?= <xavier.dupre@gmail.com>
Date: Wed, 5 May 2021 16:23:16 +0200
Subject: [PATCH 7/7] lint

---
 .../ut_documentation/test_run_notebooks_einsum_decomposition.py  | 1 -
 1 file changed, 1 deletion(-)

diff --git a/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py b/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py
index 73df7262a..3ff8c01ae 100644
--- a/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py
+++ b/_unittests/ut_documentation/test_run_notebooks_einsum_decomposition.py
@@ -11,7 +11,6 @@
 except ImportError:
     from sklearn.utils.testing import ignore_warnings
 from pyquickhelper.loghelper import fLOG
-from pyquickhelper.texthelper.version_helper import compare_module_version
 from pyquickhelper.ipythonhelper import test_notebook_execution_coverage
 from pyquickhelper.pycode import (
     add_missing_development_version, ExtTestCase)