Revert "Added tesnorizeation for avx2 based gemm. (apache#3982)" (apa…

…che#4007)

This reverts commit 23727eb.

topi/python/topi/x86/tensor_intrin.py

@@ -275,97 +275,3 @@ def _instr(index):
 
     with tvm.build_config(offset_factor=1, partition_const_loop=True):
         return tvm.decl_tensor_intrin(C.op, _intrin_func, binds={data:a_buffer, kernel:b_buffer})
-
-
-def dot_1x4x16_int8_int8_int32_avx2():
-    """
-    Int8 dot product by every 4 elements using x86 AVX2 instructions.
-    This function takes two arrays of int8 datatype -- data[4] and
-    kernel[16][4] -- and computes a dot product of data[4] with every
-    4 elements of kernels, resulting in output[16] of int32 datatype.
-    The pseudo code is as follows.
-    .. code-block:: c
-        void dot_1x4x16_int8_int8_int32(int8 data[4], int8 kernel[16][4],
-                int32 output[16]){
-            for (int i = 0; i < 16; i++){
-                out[i] = 0;
-                for (int k = 0; k < 4; k++){
-                    out[i] += data[k] * kernel[i][k]
-                }
-            }
-        }
-
-    Physically, the kernel array sits in two AVX2 vector registers and
-    the data[4] is broadcasted to AVX2 vector register. This
-    function returns a TensorIntrin that can be used to tensorize
-    a schedule.
-
-    Returns
-    -------
-    intrin : TensorIntrin
-        The AVX2 int8 TensorIntrin that can be used in tensorizing schedule
-    """
-
-    int32_lanes = 16 # 16 int32 lanes in AVX2
-    num_int8_elements = 4 # 4 int8 elements in int32
-    data = tvm.placeholder((num_int8_elements,), dtype='uint8', name='data')
-    kernel = tvm.placeholder((int32_lanes, num_int8_elements), dtype='int8', name='kernel')
-    k = tvm.reduce_axis((0, num_int8_elements), name='k')
-    C = tvm.compute((int32_lanes,),
-                    lambda i: tvm.sum(data[k].astype('int32') *
-                                      kernel[i, k].astype('int32'),
-                                      axis=k),
-                    name="C")
-
-    a_buffer = tvm.decl_buffer(data.shape, dtype='uint8', name="a_buffer",
-                               offset_factor=1,
-                               strides=[1])
-    b_buffer = tvm.decl_buffer(kernel.shape, dtype='int8', name="b_buffer",
-                               offset_factor=1,
-                               strides=[tvm.var('ldw'), 1])
-
-    def _intrin_func(ins, outs):
-        def _instr(index):
-            ib = tvm.ir_builder.create()
-            if index == 1:
-                ib.emit(outs[0].vstore(0, tvm.const(0, 'int32x16')))
-                return ib.get()
-
-            a_int8 = ins[0].vload([0], "uint8x4")
-            re_int32 = tvm.call_pure_intrin('int32', 'reinterpret', a_int8)
-            vec_ai32 = re_int32.astype('int32x8')
-            vec_a = tvm.call_pure_intrin('int8x32', 'reinterpret', vec_ai32)
-            vec_b_0 = ins[1].vload([0, 0], "int8x32")
-            vec_b_1 = ins[1].vload([8, 0], "int8x32")
-            vec_one = tvm.const(1, "int16x16")
-            pair_reduction_0 = tvm.call_llvm_intrin('int16x16',
-                                                    'llvm.x86.avx2.pmadd.ub.sw',
-                                                    tvm.const(0, 'uint32'),
-                                                    vec_a, vec_b_0)
-            quad_reduction_0 = tvm.call_llvm_intrin('int32x8',
-                                                    'llvm.x86.avx2.pmadd.wd',
-                                                    tvm.const(0, 'uint32'),
-                                                    pair_reduction_0, vec_one)
-            pair_reduction_1 = tvm.call_llvm_intrin('int16x16',
-                                                    'llvm.x86.avx2.pmadd.ub.sw',
-                                                    tvm.const(0, 'uint32'),
-                                                    vec_a, vec_b_1)
-            quad_reduction_1 = tvm.call_llvm_intrin('int32x8',
-                                                    'llvm.x86.avx2.pmadd.wd',
-                                                    tvm.const(0, 'uint32'),
-                                                    pair_reduction_1, vec_one)
-            if index == 0:
-                ib.emit(outs[0].vstore([0], quad_reduction_0))
-                ib.emit(outs[0].vstore([8], quad_reduction_1))
-            else:
-                ib.emit(outs[0].vstore([0], quad_reduction_0 + \
-                        outs[0].vload([0], 'int32x8')))
-                ib.emit(outs[0].vstore([8], quad_reduction_1 + \
-                        outs[0].vload([8], 'int32x8')))
-            return ib.get()
-
-        # body, reset, update
-        return _instr(0), _instr(1), _instr(2)
-
-    with tvm.build_config(offset_factor=1, partition_const_loop=True):
-        return tvm.decl_tensor_intrin(C.op, _intrin_func, binds={data:a_buffer, kernel:b_buffer})