[Target][TOPI] Use LLVM for x86 CPU feature lookup

include/tvm/target/target_kind.h

@@ -130,10 +130,10 @@ class TargetKind : public ObjectRef {
    */
   TVM_DLL static Optional<TargetKind> Get(const String& target_kind_name);
   TVM_DEFINE_NOTNULLABLE_OBJECT_REF_METHODS(TargetKind, ObjectRef, TargetKindNode);
-
- private:
   /*! \brief Mutable access to the container class  */
   TargetKindNode* operator->() { return static_cast<TargetKindNode*>(data_.get()); }
+
+ private:
   TVM_DLL static const AttrRegistryMapContainerMap<TargetKind>& GetAttrMapContainer(
       const String& attr_name);
   friend class TargetKindRegEntry;

python/tvm/relay/qnn/op/legalizations.py

@@ -23,7 +23,7 @@
 from tvm._ffi.base import TVMError
 from tvm.relay.qnn.op.canonicalizations import create_integer_lookup_op
 
-from ....target.x86 import target_has_sse42
+from ....target.x86 import target_has_features
 from ....topi.utils import is_target
 from .. import op as reg
 
@@ -457,8 +457,7 @@ def _shift(data, zero_point, out_dtype):
 
 def is_fast_int8_on_intel():
     """Checks whether the hardware has support for fast Int8 arithmetic operations."""
-    target = tvm.target.Target.current(allow_none=False)
-    return target_has_sse42(target.mcpu)
+    return target_has_features("sse4.2")
 
 
 # Helper function to align up given value.

python/tvm/relay/qnn/op/qnn.py

@@ -27,7 +27,7 @@
 from tvm.runtime import Object
 from tvm.target import Target
 from tvm.topi.nn.qnn import SQNN_DTYPE_TO_CODE
-from tvm.target.x86 import target_has_sse41
+from tvm.target.x86 import target_has_features
 
 from . import _make, _requantize
 
@@ -54,8 +54,9 @@ def _get_node_default_rounding():
     @staticmethod
     def _get_node_default_compute_dtype():
         target = Target.current(True)
-        if target and str(target.kind) == "llvm" and target_has_sse41(target.mcpu):
-            return "float32"
+        if target and str(target.kind) == "llvm":
+            if target_has_features("sse4.1", target):
+                return "float32"
 
         return "int64"
 

python/tvm/target/codegen.py

@@ -71,6 +71,38 @@ def llvm_get_intrinsic_name(intrin_id: int) -> str:
     return _ffi_api.llvm_get_intrinsic_name(intrin_id)
 
 
+def llvm_x86_get_archlist(only64bit=False):
+    """Get X86 CPU name list.
+
+    Parameters
+    ----------
+    only64bit : bool
+        Filter 64bit architectures.
+
+    Returns
+    -------
+    features : list[str]
+        String list of X86 architectures.
+    """
+    return _ffi_api.llvm_x86_get_archlist(only64bit)
+
+
+def llvm_x86_get_features(cpu_name):
+    """Get X86 CPU features.
+
+    Parameters
+    ----------
+    cpu_name : string
+        X86 CPU name (e.g. "skylake").
+
+    Returns
+    -------
+    features : list[str]
+        String list of X86 CPU features.
+    """
+    return _ffi_api.llvm_x86_get_features(cpu_name)
+
+
 def llvm_version_major(allow_none=False):
     """Get the major LLVM version.
 

python/tvm/target/x86.py

@@ -16,127 +16,48 @@
 # under the License.
 """Common x86 related utilities"""
 from .._ffi import register_func
-from .target import Target
-
-
-@register_func("tvm.target.x86.target_has_sse41")
-def target_has_sse41(target):
-    return (
-        target_has_sse42(target)
-        or target_has_avx(target)
-        or target_has_avx2(target)
-        or target_has_avx512(target)
-        or target_has_vnni(target)
-        or target
-        in {
-            "btver2",
-            "penryn",
-        }
-    )
-
-
-@register_func("tvm.target.x86.target_has_sse42")
-def target_has_sse42(target):
-    return (
-        target_has_avx(target)
-        or target_has_avx2(target)
-        or target_has_avx512(target)
-        or target_has_vnni(target)
-        or target
-        in {
-            "silvermont",
-            "slm",
-            "goldmont",
-            "goldmont-plus",
-            "tremont",
-            "nehalem",
-            "corei7",
-            "westmere",
-            "bdver1",
-            "bdver2",
-            "bdver3",
-            "x86-64-v2",
-        }
-    )
-
-
-@register_func("tvm.target.x86.target_has_avx")
-def target_has_avx(target):
-    return (
-        target_has_avx2(target)
-        or target_has_avx512(target)
-        or target_has_vnni(target)
-        or target in {"sandybridge", "corei7-avx", "ivybridge", "core-avx-i"}
-    )
-
-
-@register_func("tvm.target.x86.target_has_avx2")
-def target_has_avx2(target):
-    return (
-        target_has_avx512(target)
-        or target_has_vnni(target)
-        or target
-        in {
-            "haswell",
-            "core-avx2",
-            "broadwell",
-            "skylake",
-            "bdver4",
-            "znver1",
-            "znver2",
-            "znver3",
-            "x86-64-v3",
-        }
-    )
-
-
-@register_func("tvm.target.x86.target_has_avx512")
-def target_has_avx512(target):
-    return target in {
-        "skylake-avx512",
-        "skx",
-        "knl",
-        "knm",
-        "x86-64-v4",
-        "cannonlake",
-        # explicit enumeration of VNNI capable due to collision with alderlake
-        "cascadelake",
-        "icelake-client",
-        "icelake-server",
-        "rocketlake",
-        "tigerlake",
-        "cooperlake",
-        "sapphirerapids",
-    }
-
-
-@register_func("tvm.target.x86.target_has_vnni")
-def target_has_vnni(target):
-    return target in {
-        "cascadelake",
-        "icelake-client",
-        "icelake-server",
-        "rocketlake",
-        "tigerlake",
-        "cooperlake",
-        "sapphirerapids",
-        "alderlake",
-    }
-
-
-@register_func("tvm.target.x86.target_has_amx")
-def target_has_amx(target):
-    return target in {
-        "sapphirerapids",
-    }
+from . import _ffi_api
+from ..ir.container import Array
+
+
+@register_func("tvm.target.x86.target_has_features")
+def target_has_features(features, target=None):
+    """Check X86 CPU features.
+    Parameters
+    ----------
+    features : str or Array
+        Feature(s) to check.
+    target : Target
+        Optional TVM target, default `None` use the global context target.
+    Returns
+    -------
+    has_feats : bool
+        True if feature(s) are in the target arch.
+    """
+    has_feats = True
+    assert isinstance(features, (Array, str))
+    features = [features] if isinstance(features, str) else features
+    for feat in features:
+        has_feats &= _ffi_api.llvm_x86_has_feature(feat, target)
+    return has_feats
 
 
 @register_func("tvm.topi.x86.utils.get_simd_32bit_lanes")
 def get_simd_32bit_lanes():
-    mcpu = Target.current().mcpu
-    fp32_vec_len = 4
-    if target_has_avx512(mcpu):
-        fp32_vec_len = 16
-    elif target_has_avx2(mcpu):
-        fp32_vec_len = 8
-    return fp32_vec_len
+    """X86 SIMD optimal vector length lookup.
+    Parameters
+    ----------
+    Returns
+    -------
+     vec_len : int
+        The optimal vector length of CPU from the global context target.
+    """
+    vec_len = 4
+    # avx512f:  llvm.x86.avx512.addpd.w.512 (LLVM auto, added)
+    # avx512bw: llvm.x86.avx512.pmaddubs.w.512" (TVM required)
+    #         + llvm.x86.avx512.pmaddw.d.512"
+    if target_has_features(["avx512bw", "avx512f"]):
+        vec_len = 16
+    elif target_has_features("avx2"):
+        vec_len = 8
+    return vec_len

python/tvm/topi/x86/batch_matmul.py

@@ -21,7 +21,7 @@
 from tvm import autotvm, te
 from tvm.autotvm.task.space import SplitEntity
 from tvm.contrib import cblas, mkl
-from tvm.target.x86 import target_has_amx, target_has_avx512
+from tvm.target.x86 import target_has_features
 
 from .. import generic, nn
 from ..transform import layout_transform
@@ -38,8 +38,10 @@ def batch_matmul_int8_compute(cfg, x, y, *_):
     packed_y = layout_transform(y, "BNK", packed_y_layout)
     _, n_o, _, n_i, _ = packed_y.shape
     ak = te.reduce_axis((0, k), name="k")
-    mcpu = tvm.target.Target.current().mcpu
-    if target_has_avx512(mcpu):
+    # avx512f:  llvm.x86.avx512.addpd.w.512 (LLVM auto, added)
+    # avx512bw: llvm.x86.avx512.pmaddubs.w.512" (TVM required)
+    #         + llvm.x86.avx512.pmaddw.d.512"
+    if target_has_features(["avx512bw", "avx512f"]):
         attrs_info = {"schedule_rule": "batch_matmul_int8"}
     else:
         attrs_info = None
@@ -233,14 +235,16 @@ def _callback(op):
 def schedule_batch_matmul_int8(cfg, outs):
     """Schedule for batch_matmul_int8"""
     s = te.create_schedule([x.op for x in outs])
-    mcpu = tvm.target.Target.current().mcpu
 
     def _callback(op):
         if "batch_matmul_int8" in op.tag:
             layout_trans = op.input_tensors[1]
-            if target_has_amx(mcpu):
+            if target_has_features("amx-int8"):
                 batch_matmul_amx_schedule(cfg, s, op.output(0), outs[0], layout_trans)
-            elif target_has_avx512(mcpu):
+            # avx512f:  llvm.x86.avx512.addpd.w.512 (LLVM auto, added)
+            # avx512bw: llvm.x86.avx512.pmaddubs.w.512" (TVM required)
+            #         + llvm.x86.avx512.pmaddw.d.512"
+            elif target_has_features(["avx512bw", "avx512f"]):
                 batch_matmul_int8_schedule(cfg, s, op.output(0), outs[0], layout_trans)
 
     traverse_inline(s, outs[0].op, _callback)

python/tvm/topi/x86/conv2d_int8.py

@@ -20,7 +20,7 @@
 
 import tvm
 from tvm import autotvm, te
-from tvm.target.x86 import target_has_sse42
+from tvm.target.x86 import target_has_features
 
 from .. import nn, tag
 from ..generic import conv2d as conv2d_generic
@@ -49,7 +49,10 @@ def _get_default_config_int8(
     """
     if is_depthwise:
         # Fallback to FP32 default config until a VNNI schedule is defined.
-        wkl = _get_depthwise_conv2d_workload(data, kernel, strides, padding, out_dtype)
+        wkl = _get_depthwise_conv2d_workload(
+            data, kernel, strides, padding, dilation, out_dtype, layout
+        )
+
         from .depthwise_conv2d import _fallback_schedule
 
         _fallback_schedule(cfg, wkl)
@@ -81,8 +84,7 @@ def is_int8_hw_support(data_dtype, kernel_dtype):
     is_llvm_support = llvm_version >= 8
 
     # 3) Check target
-    mcpu = tvm.target.Target.current().mcpu
-    is_target_support = target_has_sse42(mcpu)
+    is_target_support = target_has_features("sse4.2")
 
     return is_dtype_support and is_llvm_support and is_target_support
 

python/tvm/topi/x86/dense.py

@@ -23,7 +23,7 @@
 from tvm import autotvm, te
 from tvm.autotvm.task.space import SplitEntity
 from tvm.contrib import cblas, dnnl, mkl
-from tvm.target.x86 import get_simd_32bit_lanes, target_has_amx, target_has_avx512
+from tvm.target.x86 import get_simd_32bit_lanes, target_has_features
 
 from .. import generic, tag
 from ..utils import get_const_tuple, traverse_inline
@@ -298,13 +298,15 @@ def dense_int8(cfg, data, weight, bias=None, out_dtype=None):
 def schedule_dense_int8(cfg, outs):
     """Create a schedule for dense__int8"""
     s = te.create_schedule([x.op for x in outs])
-    mcpu = tvm.target.Target.current().mcpu
 
     def _callback(op):
         if "dense_int8" in op.tag:
-            if target_has_amx(mcpu):
+            if target_has_features("amx-int8"):
                 dense_amx_int8_schedule(cfg, s, op.output(0), outs[0])
-            elif target_has_avx512(mcpu):
+            # avx512f:  llvm.x86.avx512.addpd.w.512 (LLVM auto, added)
+            # avx512bw: llvm.x86.avx512.pmaddubs.w.512" (TVM required)
+            #         + llvm.x86.avx512.pmaddw.d.512"
+            elif target_has_features(["avx512bw", "avx512f"]):
                 dense_int8_schedule(cfg, s, op.output(0), outs[0])
 
     traverse_inline(s, outs[0].op, _callback)
@@ -316,8 +318,10 @@ def dense_int8_compute(cfg, X, packed_w, bias=None):
     m, k = X.shape
     n_o, _, n_i, _ = packed_w.shape
     ak = te.reduce_axis((0, k), name="k")
-    mcpu = tvm.target.Target.current().mcpu
-    if target_has_avx512(mcpu):
+    # avx512f:  llvm.x86.avx512.addpd.w.512 (LLVM auto, added)
+    # avx512bw: llvm.x86.avx512.pmaddubs.w.512" (TVM required)
+    #         + llvm.x86.avx512.pmaddw.d.512"
+    if target_has_features(["avx512bw", "avx512f"]):
         target_attr = {"schedule_rule": "meta_schedule.x86.dense_int8"}
     else:
         target_attr = None

python/tvm/topi/x86/dense_alter_op.py

@@ -19,7 +19,7 @@
 
 import tvm
 from tvm import autotvm, relay, te
-from tvm.target.x86 import target_has_amx, target_has_avx512
+from tvm.target.x86 import target_has_features
 
 from .. import nn
 from ..nn import dense_alter_layout
@@ -28,9 +28,12 @@
 
 
 def check_int8_applicable(x, y, allow_padding=False):
-    mcpu = tvm.target.Target.current().mcpu
-    # TODO(vvchernov): may be also target_has_avx2 or lower?
-    simd_avai = target_has_avx512(mcpu) or target_has_amx(mcpu)
+    # avx512f:  llvm.x86.avx512.addpd.w.512 (LLVM auto, added)
+    # avx512bw: llvm.x86.avx512.pmaddubs.w.512" (TVM required)
+    #         + llvm.x86.avx512.pmaddw.d.512"
+    simd_avai = target_has_features(["avx512bw", "avx512f"])
+    simd_avai |= target_has_features("amx-int8")
+    # TODO(vvchernov): may be also target_has_features("avx2") or lower?
     return (
         simd_avai
         and "int8" in x.dtype