[GitHub] [tvm] tqchen commented on a change in pull request #9315: [TensorIR][Tutorial] Blitz course

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tqchen commented on a change in pull request #9315:
URL: https://github.com/apache/tvm/pull/9315#discussion_r731862881



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File path: gallery/tutorial/tensor_ir_blitz_course.py
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@@ -0,0 +1,135 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+"""
+.. _tir_blitz:
+
+Blitz Course to TensorIR
+========================
+**Author**: `Siyuan Feng <https://github.com/Hzfengsy>`_
+
+TensorIR is a domain specific languages for deep learning programs serving two broad purposes:
+
+- An implement for transforming and optimizing programs on various hardware backends.
+
+- An abstraction for automatic tensorized program optimization.
+
+"""
+
+import tvm
+from tvm.script import tir as T
+import numpy as np
+
+################################################################################################
+# IRModule
+# --------
+# An IRModule is the central data structure in TensorIR, which contains deep learning programs.
+# It is the basic object of interest of IR transformation and model building.
+#
+
+
+################################################################################################
+# Create an IRModule
+# ------------------
+# IRModule can be created by writing TVMScript, which is a script syntax for TVM IR. (see the ref)
+# Here is a simple module for vector add.
+#
+
+
+@tvm.script.ir_module
+class MyModule:
+    @T.prim_func
+    def main(a: T.handle, b: T.handle):
+        # We exchange data between function by handles, which are similar to pointer.
+        T.func_attr({"global_symbol": "main", "tir.noalias": True})
+        # Create buffer from handles.
+        A = T.match_buffer(a, (8,), dtype="float32")
+        B = T.match_buffer(b, (8,), dtype="float32")
+        for i in range(8):
+            # A block is an abstraction for computation.
+            with T.block("B"):
+                # Define a spatial block iterator and bind it to value i.
+                vi = T.axis.spatial(8, i)
+                B[vi] = A[vi] + 1.0
+
+
+ir_module = MyModule
+print(type(ir_module))
+print(ir_module.script())
+
+################################################################################################

Review comment:
       Add an alternative section(create IRModule from tensor expression) to quickly show how to do the same thing from te




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