[TOPI] Use cblas for dense and batch_matmul when "cblas" is in the ta…

…rget libraries (apache#3787)

* Support cblas library in dense

* start to add support for generic batch_matmul compute

* Add x86 override for batch_matmul

* Fix linting

* reset file

* Fix typos

* dummy change to re-trigger CI

python/tvm/relay/op/nn/_nn.py

@@ -73,7 +73,8 @@ def schedule_dense(attrs, outputs, target):
 @reg.register_compute("nn.batch_matmul")
 def compute_batch_matmul(attrs, inputs, out_type, target):
     """Compute definition of batch_matmul"""
-    return [topi.nn.batch_matmul(inputs[0], inputs[1])]
+    with target:
+        return [topi.nn.batch_matmul(inputs[0], inputs[1])]
 
 
 @reg.register_schedule("nn.batch_matmul")

topi/python/topi/nn/batch_matmul.py

@@ -20,8 +20,7 @@
 import tvm
 from ..util import get_const_tuple
 
-
-def batch_matmul(x, y):
+def batch_matmul_default(x, y):
     """Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
     data in batch.
 
@@ -30,7 +29,7 @@ def batch_matmul(x, y):
     x : tvm.Tensor
         3-D with shape [batch, M, K]
 
-    y : tvm.TEnsor
+    y : tvm.Tensor
         3-D with shape [batch, N, K]
 
     Returns
@@ -49,3 +48,23 @@ def batch_matmul(x, y):
     return tvm.compute((batch, M, N),
                        lambda b, i, j: tvm.sum(x[b, i, k] * y[b, j, k], axis=k),
                        tag='batch_matmul')
+
+@tvm.target.generic_func
+def batch_matmul(x, y):
+    """Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
+    data in batch.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        3-D with shape [batch, M, K]
+
+    y : tvm.Tensor
+        3-D with shape [batch, N, K]
+
+    Returns
+    -------
+    output : tvm.Tensor
+        3-D with shape [batch, M, N]
+    """
+    return batch_matmul_default(x, y)

topi/python/topi/x86/batch_matmul.py

@@ -18,10 +18,33 @@
 """x86 batch_matmul operators"""
 from __future__ import absolute_import as _abs
 import tvm
-
+from tvm.contrib import cblas
+from topi.nn import batch_matmul, batch_matmul_default
 from .. import generic
 from ..util import traverse_inline, get_const_tuple, get_max_power2_factor
 
+@batch_matmul.register(["cpu"])
+def batch_matmul_x86(x, y):
+    """Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
+    data in batch.
+
+    Parameters
+    ----------
+    x : tvm.Tensor
+        3-D with shape [batch, M, K]
+
+    y : tvm.Tensor
+        3-D with shape [batch, N, K]
+
+    Returns
+    -------
+    output : tvm.Tensor
+        3-D with shape [batch, M, N]
+    """
+    target = tvm.target.current_target()
+    if "cblas" in target.libs:
+        return cblas.batch_matmul(x, y, False, True)
+    return batch_matmul_default(x, y)
 
 @generic.schedule_batch_matmul.register(["cpu"])
 def schedule_batch_matmul(outs):
@@ -38,6 +61,10 @@ def schedule_batch_matmul(outs):
     sch: Schedule
         The computation schedule for the op.
     """
+    target = tvm.target.current_target()
+    if "cblas" in target.libs:
+        return generic.schedule_extern(outs)
+
     s = tvm.create_schedule([x.op for x in outs])
 
     def _callback(op):

topi/python/topi/x86/dense.py

@@ -20,6 +20,7 @@
 import tvm
 from tvm import autotvm
 from tvm.autotvm.task.space import SplitEntity
+from tvm.contrib import cblas
 
 from .util import get_fp32_len
 from .. import generic, tag, nn
@@ -40,29 +41,33 @@ def _declaration_dense(cfg, data, weight, bias=None, out_dtype=None):
 # Declare dense compute with packing weight into cache-friendly layout
 @autotvm.register_topi_compute(nn.dense, "cpu", "direct_pack")
 def _declaration_dense_pack(cfg, data, weight, bias=None, out_dtype=None):
-    if out_dtype is None:
-        out_dtype = data.dtype
-    batch, in_dim = get_const_tuple(data.shape)
-    out_dim, _ = get_const_tuple(weight.shape)
-    # create tuning space
-    cfg.define_split("tile_y", batch, num_outputs=3)
-    cfg.define_split("tile_x", out_dim, num_outputs=3)
-    cfg.define_split("tile_k", in_dim, num_outputs=2)
-    if cfg.is_fallback:
-        _default_dense_pack_config(cfg, batch, out_dim, in_dim)
-
-    packw_bn = cfg["tile_x"].size[-1]
-    packw_shape = (out_dim // packw_bn, in_dim, packw_bn)
-    packw = tvm.compute(packw_shape,
-                        lambda z, y, x: weight[z * packw_bn + x, y], name="packed_weight")
-
-    k = tvm.reduce_axis((0, in_dim), name="k")
-    C = tvm.compute((batch, out_dim),
-                    lambda y, x: tvm.sum(
-                        data[y, k].astype(out_dtype) *
-                        packw[x // packw_bn, k, x % packw_bn].astype(out_dtype),
-                        axis=k),
-                    tag="dense_pack")
+    target = tvm.target.current_target()
+    if "cblas" in target.libs:
+        C = cblas.matmul(data, weight, False, True)
+    else:
+        if out_dtype is None:
+            out_dtype = data.dtype
+        batch, in_dim = get_const_tuple(data.shape)
+        out_dim, _ = get_const_tuple(weight.shape)
+        # create tuning space
+        cfg.define_split("tile_y", batch, num_outputs=3)
+        cfg.define_split("tile_x", out_dim, num_outputs=3)
+        cfg.define_split("tile_k", in_dim, num_outputs=2)
+        if cfg.is_fallback:
+            _default_dense_pack_config(cfg, batch, out_dim, in_dim)
+
+        packw_bn = cfg["tile_x"].size[-1]
+        packw_shape = (out_dim // packw_bn, in_dim, packw_bn)
+        packw = tvm.compute(packw_shape,
+                            lambda z, y, x: weight[z * packw_bn + x, y], name="packed_weight")
+
+        k = tvm.reduce_axis((0, in_dim), name="k")
+        C = tvm.compute((batch, out_dim),
+                        lambda y, x: tvm.sum(
+                            data[y, k].astype(out_dtype) *
+                            packw[x // packw_bn, k, x % packw_bn].astype(out_dtype),
+                            axis=k),
+                        tag="dense_pack")
     if bias is not None:
         C = tvm.compute((batch, out_dim), lambda i, j: C[i, j] + bias[j].astype(out_dtype),
                         tag=tag.BROADCAST)
@@ -72,28 +77,32 @@ def _declaration_dense_pack(cfg, data, weight, bias=None, out_dtype=None):
 # Declare dense compute without packing weight
 @autotvm.register_topi_compute(nn.dense, "cpu", "direct_nopack")
 def _declaration_dense_nopack(cfg, data, weight, bias=None, out_dtype=None):
-    if out_dtype is None:
-        out_dtype = data.dtype
-    batch, in_dim = get_const_tuple(data.shape)
-    out_dim, _ = get_const_tuple(weight.shape)
-    # create tuning space
-    cfg.define_split("tile_x", out_dim, num_outputs=2)
-    cfg.define_split("tile_y", batch, num_outputs=2)
-    cfg.define_split("tile_k", in_dim, num_outputs=2)
-    if cfg.is_fallback:
-        _default_dense_nopack_config(cfg, batch, out_dim, in_dim)
-
-    vec = cfg["tile_k"].size[-1]
-    k = tvm.reduce_axis((0, in_dim // vec), "k")
-    CC = tvm.compute((batch, out_dim, vec),
-                     lambda z, y, x: tvm.sum(
-                         data[z, k * vec + x].astype(out_dtype) *
-                         weight[y, k * vec + x].astype(out_dtype), axis=k))
-
-    kk = tvm.reduce_axis((0, vec), "kk")
-    C = tvm.compute((batch, out_dim),
-                    lambda y, x: tvm.sum(CC[y, x, kk], axis=kk),
-                    tag="dense_nopack")
+    target = tvm.target.current_target()
+    if "cblas" in target.libs:
+        C = cblas.matmul(data, weight, False, True)
+    else:
+        if out_dtype is None:
+            out_dtype = data.dtype
+        batch, in_dim = get_const_tuple(data.shape)
+        out_dim, _ = get_const_tuple(weight.shape)
+        # create tuning space
+        cfg.define_split("tile_x", out_dim, num_outputs=2)
+        cfg.define_split("tile_y", batch, num_outputs=2)
+        cfg.define_split("tile_k", in_dim, num_outputs=2)
+        if cfg.is_fallback:
+            _default_dense_nopack_config(cfg, batch, out_dim, in_dim)
+
+        vec = cfg["tile_k"].size[-1]
+        k = tvm.reduce_axis((0, in_dim // vec), "k")
+        CC = tvm.compute((batch, out_dim, vec),
+                         lambda z, y, x: tvm.sum(
+                             data[z, k * vec + x].astype(out_dtype) *
+                             weight[y, k * vec + x].astype(out_dtype), axis=k))
+
+        kk = tvm.reduce_axis((0, vec), "kk")
+        C = tvm.compute((batch, out_dim),
+                        lambda y, x: tvm.sum(CC[y, x, kk], axis=kk),
+                        tag="dense_nopack")
     if bias is not None:
         C = tvm.compute((batch, out_dim), lambda i, j: C[i, j] + bias[j].astype(out_dtype),
                         tag=tag.BROADCAST)
@@ -116,6 +125,10 @@ def _callback(op):
 
 @autotvm.register_topi_schedule(generic.schedule_dense, "cpu", "direct_pack")
 def _schedule_dense_pack(cfg, outs):
+    target = tvm.target.current_target()
+    if "cblas" in target.libs:
+        return generic.schedule_extern(outs)
+
     s = tvm.create_schedule([x.op for x in outs])
 
     def _callback(op):
@@ -127,6 +140,10 @@ def _callback(op):
 
 @autotvm.register_topi_schedule(generic.schedule_dense, "cpu", "direct_nopack")
 def _schedule_dense_nopack(cfg, outs):
+    target = tvm.target.current_target()
+    if "cblas" in target.libs:
+        return generic.schedule_extern(outs)
+
     s = tvm.create_schedule([x.op for x in outs])
 
     def _callback(op):