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From GitBox <...@apache.org>
Subject [GitHub] [incubator-tvm] areusch commented on a change in pull request #5417: [RUNTIME][uTVM] AutoTVM + uTVM for Cortex-M7
Date Mon, 27 Apr 2020 20:51:33 GMT

areusch commented on a change in pull request #5417:
URL: https://github.com/apache/incubator-tvm/pull/5417#discussion_r416139282



##########
File path: topi/python/topi/arm_cpu/cortex_m7/micro_kernel/gemm.py
##########
@@ -0,0 +1,221 @@
+# 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.
+# pylint: disable=invalid-name, no-value-for-parameter
+"""Defines gemm intrinsics for SIMD matrix multiplication."""
+
+import random
+import string
+
+import tvm
+from tvm import te
+
+##########################
+# MxKxN MatMul Intrinsic #
+##########################
+
+# NOTE this is transposed matmul (A * B^T)
+def intrin_gemm_MxKxN(M, K, N, in_dtype, out_dtype):
+    """Defines a SIMD-accelerated transposed matmul."""
+    # we generate a unique ID for every intrinsic definition, to prevent name
+    # collisions in the generated source (e.g., if there are multiple operators
+    # in the same module that use the same intrinsic)
+    #
+    # TODO(weberlo, areusch): to cut down on memory usage, we should cache each intrinsic
+    # instantiation and include it only once, eliminating the need for unique
+    # IDs
+    UNIQ_ID_LEN = 8
+    uniq_id = ''.join(random.choices(string.ascii_uppercase, k=UNIQ_ID_LEN))
+
+    if isinstance(M, tvm.tir.IntImm):
+        M = M.value
+    if isinstance(K, tvm.tir.IntImm):
+        K = K.value
+    if isinstance(N, tvm.tir.IntImm):
+        N = N.value
+    assert K % 4 == 0
+    # TODO(weberlo, areusch): support more dtypes?
+    assert in_dtype == 'int8'
+    assert out_dtype == 'int32'
+    A = te.placeholder((M, K), name='a', dtype=in_dtype)
+    B = te.placeholder((N, K), name='b', dtype=in_dtype)
+    k = te.reduce_axis((0, K), name='k')
+    C = te.compute(
+        (M, N),
+        lambda i, j: te.sum(A[i, k].astype(out_dtype) * B[j, k].astype(out_dtype), axis=k),
+        name='c')
+    A_buf = tvm.tir.decl_buffer(
+        A.shape, A.dtype,
+        name="A",
+        offset_factor=1,
+        strides=[te.var("A_s"), 1])
+    B_buf = tvm.tir.decl_buffer(
+        B.shape, B.dtype,
+        name="B",
+        offset_factor=1,
+        strides=[te.var("B_s"), 1])
+    C_buf = tvm.tir.decl_buffer(
+        C.shape, C.dtype,
+        name="C",
+        offset_factor=1,
+        strides=[te.var("C_s"), 1])
+    def intrin_func(ins, outs):
+        aa, bb = ins
+        cc = outs[0]
+        def _reduce_update():
+            ib = tvm.tir.ir_builder.create()
+            ib.emit(tvm.tir.call_extern("int32", f"gemm_{M}x{K}x{N}_update_{uniq_id}",
+                                        aa.access_ptr("r"),
+                                        bb.access_ptr("r"),
+                                        cc.access_ptr("w"),
+                                        aa.strides[0],
+                                        bb.strides[0],
+                                        cc.strides[0]))
+            return ib.get()
+        def _reduce_reset():
+            ib = tvm.tir.ir_builder.create()
+            ib.emit(tvm.tir.call_extern("int32", f"gemm_{M}x{K}x{N}_reset_{uniq_id}",
+                                        cc.access_ptr("w"),
+                                        cc.strides[0]))
+            return ib.get()
+        def _body():
+            ib = tvm.tir.ir_builder.create()
+            # # NOTE we need the reset in the body for cases where the buffer

Review comment:
       done




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