[Relay] Legalize and AlterOpLayout for Int8 Intel.

tests/python/relay/test_op_level2.py

@@ -541,18 +541,35 @@ def test_upsampling():
 
 
 def test_conv2d_int8_intrinsics():
-    def _compile(input_dtype, weight_dtype, output_dtype, target):
-        n, ic, h, w, oc, ch, cw = 1, 16, 224, 224, 32, 3, 3
-        x = relay.var("x", relay.TensorType((n, ic, h, w), input_dtype))
-        w = relay.var("w", relay.TensorType((oc, ic, ch, cw), weight_dtype))
+    def _compile(ic, oc, target, data_layout, kernel_layout, dtypes):
+        input_dtype, weight_dtype, output_dtype = dtypes
+
+        n, h, w, ch, cw = 1, 64, 64, 3, 3
+        if data_layout == 'NCHW':
+            x = relay.var("x", relay.TensorType((n, ic, h, w), input_dtype))
+        elif data_layout == 'NHWC':
+            x = relay.var("x", relay.TensorType((n, h, w, ic), input_dtype))
+        else:
+            raise ValueError('Not supported')
+
+        if kernel_layout == 'OIHW':
+            kernel_shape = (oc, ic, ch, cw)
+        elif kernel_layout == 'HWIO':
+            kernel_shape = (ch, cw, ic, oc)
+        else:
+            raise ValueError('Not supported')
+
+        w = relay.var("w", relay.TensorType(kernel_shape, weight_dtype))
         y = relay.nn.conv2d(x, w,
                             kernel_size=(ch, cw),
                             channels=oc,
                             padding=(1, 1),
                             dilation=(1, 1),
+                            data_layout=data_layout,
+                            kernel_layout=kernel_layout,
                             out_dtype=output_dtype)
         func = relay.Function([x, w], y)
-        wdata = np.random.rand(oc, ic, ch, cw) * 10
+        wdata = np.random.rand(*kernel_shape) * 10
         parameters = {"w": tvm.nd.array(wdata.astype(weight_dtype))}
         with relay.build_config(opt_level=3):
             graph, lib, params = relay.build(func, target, params=parameters)
@@ -564,37 +581,59 @@ def _compile(input_dtype, weight_dtype, output_dtype, target):
     name = "llvm.x86.avx512.pmaddubs.w.512"
     llvm_id = tvm.codegen.llvm_lookup_intrinsic_id(name)
     if llvm_id != 0:
-        # Intel Int8 instruction need uint8 data and int8 kernel
-        asm = _compile(input_dtype="uint8",
-                       weight_dtype="int8",
-                       output_dtype="int32",
-                       target=target)
-        # Check that intrinisic is present in the assembly.
+        fast_int8_dtypes = ('uint8', 'int8', 'int32')
+        # Sweep the input channels to check int8 robustness
+        for ic in range(1, 24):
+            asm = _compile(ic=ic, oc=32, target=target, data_layout="NCHW", kernel_layout='OIHW',
+                           dtypes=fast_int8_dtypes)
+            assert "pmaddubs" in asm
+
+        for ic in range(1, 24):
+            asm = _compile(ic=ic, oc=32, target=target, data_layout="NHWC", kernel_layout='HWIO',
+                           dtypes=fast_int8_dtypes)
+            assert "pmaddubs" in asm
+
+
+        # Sweep the output channels to check int8 robustness
+        for oc in range(2, 24):
+            asm = _compile(ic=16, oc=oc, target=target, data_layout="NCHW", kernel_layout='OIHW',
+                           dtypes=fast_int8_dtypes)
+            assert "pmaddubs" in asm
+
+        for oc in range(2, 24):
+            asm = _compile(ic=16, oc=oc, target=target, data_layout="NHWC", kernel_layout='HWIO',
+                           dtypes=fast_int8_dtypes)
+            assert "pmaddubs" in asm
+
+        # Check that both non-divisible oc and ic work
+        asm = _compile(ic=17, oc=29, target=target, data_layout="NCHW", kernel_layout='OIHW',
+                       dtypes=fast_int8_dtypes)
+        assert "pmaddubs" in asm
+
+        asm = _compile(ic=17, oc=29, target=target, data_layout="NHWC", kernel_layout='HWIO',
+                       dtypes=fast_int8_dtypes)
         assert "pmaddubs" in asm
 
         # Ensure that code is generated when datatypes are not HW supported.
-        asm = _compile(input_dtype="int8",
-                       weight_dtype="int8",
-                       output_dtype="int32",
-                       target=target)
+        dtypes = ('int8', 'int8', 'int32')
+        asm = _compile(ic=16, oc=32, target=target, data_layout="NHWC", kernel_layout='HWIO',
+                       dtypes=dtypes)
         # Check that intrinisic is not present in the assembly.
         assert "pmaddubs" not in asm
 
         # Ensure that code is generated when datatypes are not HW supported.
-        asm = _compile(input_dtype="uint8",
-                       weight_dtype="uint8",
-                       output_dtype="int32",
-                       target=target)
+        dtypes = ('uint8', 'uint8', 'int32')
+        asm = _compile(ic=16, oc=32, target=target, data_layout="NHWC", kernel_layout='HWIO',
+                       dtypes=dtypes)
         # Check that intrinisic is not present in the assembly.
         assert "pmaddubs" not in asm
 
     # Check that a vectorized instruction is generated for older Intel
     # generations, because we default to NCHWc layout.
     target = "llvm -mcpu=core-avx2"
-    asm = _compile(input_dtype="int8",
-                  weight_dtype="int8",
-                  output_dtype="int32",
-                  target=target)
+    fast_int8_dtypes = ('uint8', 'int8', 'int32')
+    asm = _compile(ic=16, oc=32, target=target, data_layout="NCHW", kernel_layout='OIHW',
+                   dtypes=fast_int8_dtypes)
     # Check that vector int mult and add instructions are generated.
     assert "vpmulld" in asm and "vpadd" in asm
 

topi/python/topi/nn/conv2d.py

@@ -151,7 +151,7 @@ def _get_workload(data, kernel, stride, padding, out_dtype, data_layout='NCHW'):
     if data_layout == 'NCHW':
         CO, CIG, KH, KW = [x.value for x in kernel.shape]
     else:
-        KH, KW, CO, CIG = [x.value for x in kernel.shape]
+        KH, KW, CIG, CO = [x.value for x in kernel.shape]
 
     HPAD, WPAD, _, _ = get_pad_tuple(padding, kernel)
     GRPS = CI // CIG

topi/python/topi/x86/__init__.py

@@ -17,3 +17,4 @@
 from .roi_align import roi_align_nchw
 from .conv2d_transpose import _schedule_conv2d_transpose_nchw
 from .sparse import *
+from .conv2d_alter_op import *

topi/python/topi/x86/conv2d.py

@@ -26,40 +26,16 @@
 from tvm.autotvm.task import get_config
 from .. import generic, tag
 from .. import nn
-from ..util import get_const_tuple, get_shape
-from ..nn.conv2d import conv2d, conv2d_NCHWc, conv2d_NCHWc_int8, \
-    conv2d_alter_layout, conv2d_infer_layout, _get_workload as _get_conv2d_workload
+from ..nn.conv2d import conv2d, conv2d_NCHWc, \
+    conv2d_infer_layout, _get_workload as _get_conv2d_workload
 from ..nn.depthwise_conv2d import _get_workload as _get_depthwise_conv2d_workload
-from ..nn.depthwise_conv2d import depthwise_conv2d_NCHWc, depthwise_conv2d_nchw
 from ..nn.pad import pad
+from ..util import get_const_tuple
 
 from . import conv2d_avx_1x1, conv2d_avx_common
 
 logger = logging.getLogger('topi')
 
-def _is_int8_hw_support(data_dtype, kernel_dtype, target):
-    """
-    Checks to ensure that we can use Intel DLBoost instructions
-    1) The datatypes are correct.
-    2) LLVM version has support for the instructions.
-    3) Target is skylake and above.
-    """
-    # 1) Check datatypes
-    is_dtype_support = data_dtype == 'uint8' and kernel_dtype == 'int8'
-
-    # 2) Check LLVM support
-    llvm_intrin_fast_int8 = "llvm.x86.avx512.pmaddubs.w.512"
-    llvm_id = tvm.codegen.llvm_lookup_intrinsic_id(llvm_intrin_fast_int8)
-    is_llvm_support = llvm_id != 0
-
-    # 3) Check target
-    is_target_support = False
-    for opt in target.options:
-        if opt == '-mcpu=skylake-avx512':
-            is_target_support = True
-
-    return is_dtype_support and is_llvm_support and is_target_support
-
 def _get_default_config(cfg, data, kernel, strides, padding, out_dtype, is_depthwise=False,
                         layout='NCHW'):
     """
@@ -353,133 +329,6 @@ def _topi_nn_conv2d_NCHWc(*args, **kwargs):
     return s, [new_data, new_kernel, C]
 
 
-@conv2d_alter_layout.register("cpu")
-def _alter_conv2d_layout(attrs, inputs, tinfo, F):
-
-    copy_inputs = [s for s in inputs]
-    new_attrs = {k : attrs[k] for k in attrs.keys()}
-
-    if F.__name__ == 'tvm.relay.op':
-        # Derive channels for frontends (e.g ONNX) that miss "channel" field.
-        new_attrs["channels"] = inputs[1].checked_type.shape[attrs['kernel_layout'].index('O')]
-
-    data, kernel = tinfo[0], tinfo[1]
-    batch_size, in_channel, height, width = get_const_tuple(data.shape)
-
-    groups = attrs.get_int("groups")
-    out_channel = attrs.get_int("channels") \
-        if F.__name__ == 'nnvm.symbol' else new_attrs["channels"]
-    padding = attrs.get_int_tuple("padding")
-    strides = attrs.get_int_tuple("strides")
-    dilation = attrs.get_int_tuple("dilation")
-    out_dtype = attrs["out_dtype"]
-
-    layout_name = 'layout' if F.__name__ == 'nnvm.symbol' else 'data_layout'
-
-    layout = attrs[layout_name]
-    kh, kw = attrs.get_int_tuple("kernel_size")
-
-    dtype = data.dtype
-    out_dtype = dtype if out_dtype in ("same", "") else out_dtype
-
-    kshape = get_shape(kernel.shape, attrs["kernel_layout"], "OIHW")
-    is_depthwise = groups == kshape[0] and kshape[1] == 1
-
-    # only optimize for NCHW
-    if layout != 'NCHW' or attrs["kernel_layout"] != "OIHW":
-        return None
-
-    if groups != 1 and not is_depthwise:
-        return None
-
-    dispatch_ctx = autotvm.task.DispatchContext.current
-    target = tvm.target.current_target()
-    # query schedule and fallback if necessary
-    workload = autotvm.task.args_to_workload(
-        [data, kernel, strides, padding, dilation, out_dtype], depthwise_conv2d_nchw) \
-        if is_depthwise else \
-        autotvm.task.args_to_workload(
-            [data, kernel, strides, padding, dilation, layout, out_dtype], conv2d)
-    cfg = dispatch_ctx.query(target, workload)
-    if cfg.is_fallback:
-        _get_default_config(cfg, data, kernel, strides, padding, out_dtype, is_depthwise)
-
-    ic_bn, oc_bn = cfg["tile_ic"].size[-1], cfg["tile_oc"].size[-1]
-
-    new_attrs[layout_name] = 'NCHW%dc' % ic_bn
-    new_attrs['out_layout'] = 'NCHW%dc' % oc_bn
-
-    new_data = tvm.placeholder((batch_size, in_channel//ic_bn, height, width, ic_bn),
-                               dtype=data.dtype)
-
-    if is_depthwise:
-        new_attrs['kernel_layout'] = 'OIHW1i%do' % oc_bn
-        # Store altered operator's config
-        new_kernel = tvm.placeholder((out_channel//oc_bn, 1, kh, kw, 1, oc_bn), dtype=kernel.dtype)
-        new_workload = autotvm.task.args_to_workload(
-            [new_data, new_kernel, strides, padding, dilation, new_attrs[layout_name],
-             new_attrs['out_layout'], out_dtype], depthwise_conv2d_NCHWc)
-        dispatch_ctx.update(target, new_workload, cfg)
-        if F.__name__ == 'nnvm.symbol':
-            logging.warning("Use native layout for depthwise convolution on NNVM.")
-            return None
-        return F.nn.contrib_depthwise_conv2d_nchwc(*copy_inputs, **new_attrs)
-
-    if _is_int8_hw_support(data.dtype, kernel.dtype, target):
-        # Convert kernel data layout from 4D to 7D
-        n_elems = 4
-        out_channel, _, kh, kw = get_const_tuple(kernel.shape)
-        data_expr, kernel_expr = inputs
-        kernel_IHWO = F.transpose(kernel_expr, axes=(1, 2, 3, 0))
-        kernel_IHWOo = F.reshape(kernel_IHWO, (in_channel, kh, kw, out_channel//oc_bn, oc_bn))
-        kernel_OHWoI = F.transpose(kernel_IHWOo, axes=(3, 1, 2, 4, 0))
-        kernel_OHWoIi = F.reshape(kernel_OHWoI, (out_channel//oc_bn, kh, kw, oc_bn,
-                                                 in_channel//ic_bn, ic_bn))
-        kernel_OHWoIie = F.reshape(kernel_OHWoIi, (out_channel//oc_bn, kh, kw, oc_bn,
-                                                   in_channel//ic_bn, ic_bn//n_elems, n_elems))
-        kernel_OIHWioe = F.transpose(kernel_OHWoIie, axes=(0, 4, 1, 2, 5, 3, 6))
-        copy_inputs = [data_expr, kernel_OIHWioe]
-
-        # Store altered operator's config. New kernel layout OIHWio4
-        new_kernel = tvm.placeholder((out_channel // oc_bn,
-                                      in_channel // ic_bn,
-                                      kh,
-                                      kw,
-                                      ic_bn // n_elems,
-                                      oc_bn,
-                                      n_elems), dtype=kernel.dtype)
-
-        new_workload = autotvm.task.args_to_workload([new_data,
-                                                      new_kernel,
-                                                      strides,
-                                                      padding,
-                                                      dilation,
-                                                      new_attrs[layout_name],
-                                                      new_attrs['out_layout'],
-                                                      out_dtype],
-                                                     conv2d_NCHWc_int8)
-        dispatch_ctx.update(target, new_workload, cfg)
-        if F.__name__ == 'nnvm.symbol':
-            logging.warning("Use native layout for int8 convolution on NNVM.")
-            return None
-        return F.nn.contrib_conv2d_nchwc_int8(*copy_inputs, **new_attrs)
-
-    out_channel, _, kh, kw = get_const_tuple(kernel.shape)
-    # (oc, ic, h, w) -> (OC, IC, h, w, ic, oc)
-    new_attrs['kernel_layout'] = 'OIHW%di%do' % (ic_bn, oc_bn)
-    # Store altered operator's config
-    new_kernel = tvm.placeholder((out_channel//oc_bn, in_channel//ic_bn,
-                                  kh, kw, ic_bn, oc_bn), dtype=kernel.dtype)
-    new_workload = autotvm.task.args_to_workload(
-        [new_data, new_kernel, strides, padding, dilation, new_attrs[layout_name],
-         new_attrs['out_layout'], out_dtype], conv2d_NCHWc)
-    dispatch_ctx.update(target, new_workload, cfg)
-
-    if F.__name__ == 'nnvm.symbol':
-        return F.contrib.conv2d_NCHWc(*copy_inputs, **new_attrs)
-    return F.nn.contrib_conv2d_nchwc(*copy_inputs, **new_attrs)
-
-
 @conv2d_infer_layout.register("cpu")
 def _conv2d_infer_layout(workload, cfg):
     _, data, kernel, strides, padding, dilation, layout, dtype = workload