[ARM] Fix int8 NCHWc compute and alter layout (apache#10839)

This PR fixes a bug in TE ARM int8 compute for NCHWc conv2d, introduced in apache#10310. The compute itself, not the schedule, is broken for the following reasons:

* We are using `n_elems = 8` in https://github.com/apache/tvm/blob/e9091d6c68d5d70c28881e5c75bfe72e385c1f4d/python/tvm/topi/arm_cpu/conv2d_alter_op.py#L350. Thus, the innermost axis of the transformed kernel has extent 8: https://github.com/apache/tvm/blob/e9091d6c68d5d70c28881e5c75bfe72e385c1f4d/python/tvm/topi/arm_cpu/conv2d_alter_op.py#L375
* In the TE compute, we iterate over the innermost axis `ic_s_inner` of the kernel at https://github.com/apache/tvm/blob/f6f252f0abc8f621a96506739f9534083d1fe213/python/tvm/topi/nn/conv2d.py#L577. `ic_s_inner` has extent `n_elems` according to https://github.com/apache/tvm/blob/f6f252f0abc8f621a96506739f9534083d1fe213/python/tvm/topi/nn/conv2d.py#L566. `n_elems` is 4 by default according to https://github.com/apache/tvm/blob/f6f252f0abc8f621a96506739f9534083d1fe213/python/tvm/topi/nn/conv2d.py#L478
* The ARM code that calls this compute does not explicitly pass `n_elems`, according to https://github.com/apache/tvm/blob/e9091d6c68d5d70c28881e5c75bfe72e385c1f4d/python/tvm/topi/arm_cpu/conv2d_int8.py#L106-L108
* Thus, even though the innermost axis of the kernel has extent 8, the TE compute only loops over `n_elems = 4` of the input channel dimension. 

Initially, I tried to keep `n_elems = 8` in alter layout and fix the intrinsic definition. But `n_elems = 8` breaks tensorization pattern matching, since now the compute is doing 4x8 innermost loop but this intrinsic is supposed to do 4x4 dot product, see https://github.com/apache/tvm/blob/7896108fc41663a1fecbb52345194a93278e9e28/python/tvm/topi/arm_cpu/tensor_intrin.py#L467-L479. Setting `num_int8_elements = 8` there does fix the tensorize pattern matching, but the result was still incorrect.

Rather than fixing the intrin implementation in https://github.com/apache/tvm/blob/7896108fc41663a1fecbb52345194a93278e9e28/python/tvm/topi/arm_cpu/tensor_intrin.py#L492 to adapt for 4x8 dot product, I settled on setting `n_elems = 4` in alter layout. It turned out this change is enough to get the correct output. Moreover, `n_elems = 8` is simply wrong for the dot product path in https://github.com/apache/tvm/blob/7896108fc41663a1fecbb52345194a93278e9e28/python/tvm/topi/arm_cpu/conv2d_int8.py#L154-L155 which computes 4x4 dot product in one instruction. 

@tkonolige I suggest doing perf benchmark again, since the numbers in apache#10310 are invalid.

cc @mbrookhart @Mousius  @junrushao1994 @vinx13

python/tvm/topi/arm_cpu/conv2d_alter_op.py

@@ -347,7 +347,7 @@ def _alter_conv2d_layout(attrs, inputs, tinfos, out_type):
         batch_size, in_channel, height, width = get_const_tuple(data_tensor.shape)
         out_channel, _, kh, kw = get_const_tuple(kernel_tensor.shape)
 
-        n_elems = 8
+        n_elems = 4
 
         if cfg.is_fallback:
             _get_default_config_int8(

python/tvm/topi/arm_cpu/conv2d_int8.py

@@ -57,7 +57,7 @@ def conv2d_NCHWc_int8(cfg, data, kernel, strides, padding, dilation, layout, out
         n, ic_chunk, ih, iw, ic_bn = get_const_tuple(data.shape)
         in_channel = ic_chunk * ic_bn
 
-        oc_chunk, ic_chunk, kh, kw, ic_bn, oc_bn, n_elems = get_const_tuple(kernel.shape)
+        oc_chunk, ic_chunk, kh, kw, ic_bn, oc_bn, _ = get_const_tuple(kernel.shape)
         num_filter = oc_chunk * oc_bn
     else:
         # data is nchw, implicitly treat it as nchw1c
@@ -103,8 +103,10 @@ def conv2d_NCHWc_int8(cfg, data, kernel, strides, padding, dilation, layout, out
     if len(data.shape) == 4:
         data, kernel = _pack_data(cfg, data, kernel)
 
+    n_elems = int(kernel.shape[-1])
+
     return nn.conv2d_NCHWc_int8(
-        data, kernel, strides, padding, dilation, layout, out_layout, out_dtype
+        data, kernel, strides, padding, dilation, layout, out_layout, out_dtype, n_elems=n_elems
     )
 
 
@@ -149,7 +151,8 @@ def _callback(op):
 
             args = [s, cfg, data_vec, kernel_vec, conv_out, outs[0]]
             # int8 conv kernel is 7-dim
-            _, _, kh, kw, _, _, _ = get_const_tuple(kernel_vec.shape)
+            _, _, kh, kw, _, _, n_elems = get_const_tuple(kernel_vec.shape)
+            assert n_elems == 4
             dtype = "uint" if data.dtype == "uint8" else "int"
             if is_dotprod_available():
                 intrin = dot_int8_int8_int32_neon_82(int32_lanes=4, dtype=dtype)

python/tvm/topi/arm_cpu/tensor_intrin.py

@@ -614,21 +614,22 @@ def _instr(index):
                 ib.emit(outs[0].vstore(0, tvm.tir.const(0, int_32xl)))
                 return ib.get()
 
-            def pairwise_add_mul(idx):
-                # this broadcasts data to the vector size
-                a_int8 = ins[0].vload([0], "int8x4")
-                re_int32 = tvm.tir.call_intrin("int32", "tir.reinterpret", a_int8)
-                vec_ai32 = re_int32.astype("int32x2")
-                vec_a = tvm.tir.call_intrin(int_8xl, "tir.reinterpret", vec_ai32)
+            # this broadcasts data to the vector size
+            a_int8 = ins[0].vload([0], "int8x4")
+            re_int32 = tvm.tir.call_intrin("int32", "tir.reinterpret", a_int8)
+            vec_ai32 = re_int32.astype("int32x2")
+            vec_a = tvm.tir.call_intrin(int_8xl, "tir.reinterpret", vec_ai32)
 
-                vec_b = ins[1].vload([idx * 2, 0], int_8xl)  # we take two inputs at a time
+            vec_b = ins[1].vload([0, 0], "int8x16")
 
+            def pairwise_add_mul(extract_half):
+                vec_b_half = tvm.tir.call_intrin("int8x8", extract_half, vec_b)
                 multiply = tvm.tir.call_llvm_pure_intrin(
                     "int16x8",
                     "llvm.aarch64.neon.smull.v8i16",  # saturating pairwise multiplication
                     tvm.tir.const(2, "uint32"),
                     vec_a,
-                    vec_b,
+                    vec_b_half,
                 )
                 pairwise_reduction = tvm.tir.call_llvm_pure_intrin(
                     "int32x4",
@@ -638,8 +639,8 @@ def pairwise_add_mul(idx):
                 )
                 return pairwise_reduction
 
-            pair_1 = pairwise_add_mul(0)
-            pair_2 = pairwise_add_mul(1)
+            pair_1 = pairwise_add_mul("tir.vectorlow")
+            pair_2 = pairwise_add_mul("tir.vectorhigh")
             quad_reduction = tvm.tir.call_llvm_pure_intrin(
                 "int32x4",
                 "llvm.aarch64.neon.addp.v4i32",

python/tvm/topi/nn/conv2d.py

@@ -486,7 +486,6 @@ def conv2d_NCHWc_int8(
     oc_chunk, ic_chunk_group, kernel_height, kernel_width, _, oc_bn, _ = get_const_tuple(
         kernel.shape
     )
-    num_filter = oc_chunk * oc_bn
     groups = ic_chunk // ic_chunk_group
 
     dilated_kernel_h = (kernel_height - 1) * dilation_h + 1

python/tvm/topi/x86/conv2d_int8.py

@@ -120,7 +120,7 @@ def _pack_data(cfg, data, kernel):
     kernel = te.compute(
         (oc_chunk, ic_chunk, kh, kw, ic_bn // n_elems, oc_bn, n_elems),
         lambda occ, icc, k_h, k_w, icbc, ocb, icbb: kernel[
-            occ * oc_bn + ocb, icc * ic_bn + icbc * ic_bn // n_elems + icbb, k_h, k_w
+            occ * oc_bn + ocb, icc * ic_bn + icbc * n_elems + icbb, k_h, k_w
         ],
         name="kernel_vec",
     )

tests/python/topi/python/test_topi_conv2d_int8.py

@@ -21,7 +21,6 @@
 import tvm
 from tvm import te
 from tvm import autotvm
-from tvm.autotvm.task.space import FallbackConfigEntity
 from tvm import topi
 import tvm.topi.testing
 from tvm.contrib.pickle_memoize import memoize
@@ -34,6 +33,7 @@
 from common import Int8Fallback
 import tvm.testing
 import pytest
+import platform
 
 
 def compile_conv2d_NHWC_gemm_int8_arm(
@@ -299,7 +299,6 @@ def get_ref_data():
 
         a_np, w_np, b_np, c_np = get_ref_data()
 
-        print("Running on target: %s" % target)
         with tvm.target.Target(target):
             C = compute(
                 A,
@@ -311,8 +310,6 @@ def get_ref_data():
                 "NCHW",
                 out_dtype,
             )
-            print(C.shape)
-            print(bias.shape)
             if add_bias:
                 C = topi.add(C, bias)
             if add_relu:
@@ -342,6 +339,8 @@ def get_ref_data():
         if build_only:
             return
 
+        print("Running on target: %s" % target)
+
         func(*run_args)
 
         tvm.testing.assert_allclose(c.numpy(), c_np, rtol=1e-5)
@@ -364,14 +363,15 @@ def get_ref_data():
         # ),
     ]
 
-    # TODO(tvm-team): Properly run ARM code on CI aarch64 environment
+    build_only_aarch64 = platform.machine() != "aarch64"
+
     targets.append(
         (
             "llvm -device arm_cpu -mtriple aarch64-linux-gnu -mattr=+neon,+v8.2a,+dotprod",
             topi.arm_cpu.conv2d_NCHWc_int8,
             topi.arm_cpu.schedule_conv2d_NCHWc_int8,
             8,
-            True,
+            build_only_aarch64,
         )
     )
 
@@ -382,7 +382,7 @@ def get_ref_data():
                 topi.arm_cpu.conv2d_NCHWc_int8,
                 topi.arm_cpu.schedule_conv2d_NCHWc_int8,
                 8,
-                True,
+                build_only_aarch64,
             )
         )