[CUDA][PASS]Legalize tensorcore

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

@@ -52,6 +52,27 @@
 reg.register_pattern("nn.log_softmax", OpPattern.OPAQUE)
 
 
+@reg.register_legalize("nn.dense")
+def legalize_dense(attrs, inputs, types):
+    """Legalize dense op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    return topi.nn.dense_legalize(attrs, inputs, types)
+
+
 # dense
 reg.register_strategy("nn.dense", strategy.dense_strategy)
 reg.register_pattern("nn.dense", reg.OpPattern.OUT_ELEMWISE_FUSABLE)
@@ -67,6 +88,27 @@ def compute_fifo_buffer(attrs, inputs, out_type):
 reg.register_pattern("nn.fifo_buffer", OpPattern.OPAQUE)
 
 
+@reg.register_legalize("nn.batch_matmul")
+def legalize_batch_matmul(attrs, inputs, types):
+    """Legalize batch_matmul op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    return topi.nn.batch_matmul_legalize(attrs, inputs, types)
+
+
 # batch_matmul
 reg.register_strategy("nn.batch_matmul", strategy.batch_matmul_strategy)
 reg.register_pattern("nn.batch_matmul", reg.OpPattern.OUT_ELEMWISE_FUSABLE)

python/tvm/topi/cuda/__init__.py

@@ -54,4 +54,5 @@
 from .conv2d_hwnc_tensorcore import *
 from .correlation import *
 from .sparse import *
+from . import tensorcore_alter_op
 from .argwhere import *

python/tvm/topi/cuda/conv2d_alter_op.py

@@ -24,8 +24,10 @@
 from .. import nn
 from ..utils import get_const_tuple
 from .conv2d_winograd import _infer_tile_size
+from .tensorcore_alter_op import pad_to_tensorcore
 from ..nn import conv2d_legalize
 
+
 logger = logging.getLogger("topi")
 
 
@@ -345,4 +347,50 @@ def _conv2d_legalize(attrs, inputs, arg_types):
             else:
                 out = relay.nn.conv2d(data, kernel, **new_attrs)
             return out
+    elif data_dtype in ["float16"]:  # todo: support int8/int4
+        if data_layout == "NHWC" and kernel_layout == "HWIO":
+            batch = data_tensor.shape[0].value
+            in_channel = data_tensor.shape[3].value
+            out_channel = kernel_tensor.shape[3].value
+
+            if (
+                (batch % 8 == 0 and in_channel % 16 == 0 and out_channel % 32 == 0)
+                or (batch % 16 == 0 and in_channel % 16 == 0 and out_channel % 16 == 0)
+                or (batch % 32 == 0 and in_channel % 16 == 0 and out_channel % 8 == 0)
+            ):
+                # no need to pad
+                return None
+
+            (db, di, do), extra_flops = pad_to_tensorcore(batch, in_channel, out_channel)
+
+            if extra_flops > 2:
+                logger.info("conv2d pad_to_tensorcore skipped, extra_flops %s", extra_flops)
+                return None
+
+            logger.info("conv2d pad_to_tensorcore, extra_flops %s", extra_flops)
+
+            # Pad batch size
+            if db != 0:
+                data = relay.nn.pad(data, pad_width=((0, db), (0, 0), (0, 0), (0, 0)))
+
+            # Pad input channel
+            if di != 0:
+                data = relay.nn.pad(data, pad_width=((0, 0), (0, 0), (0, 0), (0, di)))
+                kernel = relay.nn.pad(kernel, pad_width=((0, 0), (0, 0), (0, di), (0, 0)))
+
+            # Pad output channel
+            if do != 0:
+                kernel = relay.nn.pad(kernel, pad_width=((0, 0), (0, 0), (0, 0), (0, do)))
+
+            if do != 0:
+                new_out_channel = out_channel + do
+                new_attrs["channels"] = new_out_channel
+
+            out = relay.nn.conv2d(data, kernel, **new_attrs)
+
+            if db != 0 or do != 0:
+                original_out_shape = [x.value for x in output_tensor.shape]
+                out = relay.strided_slice(out, begin=[0, 0, 0, 0], end=original_out_shape)
+
+            return out
     return None

python/tvm/topi/cuda/tensorcore_alter_op.py

@@ -0,0 +1,204 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=invalid-name,unused-variable,unused-argument
+"""Tensorcore alter op and legalize functions for cuda backend"""
+
+import logging
+import math
+from tvm import relay
+
+from .. import nn
+
+logger = logging.getLogger("topi")
+
+
+@nn.batch_matmul_legalize.register("cuda")
+def _batch_matmul_legalize(attrs, inputs, arg_types):
+    """Legalizes batch_matmul op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    arg_types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # Collect the input tensors.
+    x_tensor, y_tensor = arg_types[0], arg_types[1]
+    dtype = x_tensor.dtype
+
+    # Collect the output tensor.
+    output_tensor = arg_types[2]
+
+    # Collect the input exprs.
+    x, y = inputs
+
+    # Pad input and output channels to use tensorcore schedule.
+    if dtype in ["float16"]:  # todo: support int8/int4
+        B, M, K = x_tensor.shape
+        B, N, K = y_tensor.shape
+        M = M.value
+        K = K.value
+        N = N.value
+
+        # The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32)
+        if (
+            (M % 8 == 0 and K % 16 == 0 and N % 32 == 0)
+            or (M % 16 == 0 and K % 16 == 0 and N % 16 == 0)
+            or (M % 32 == 0 and K % 16 == 0 and N % 8 == 0)
+        ):
+            # no need to pad
+            return None
+
+        (dm, dk, dn), extra_flops = pad_to_tensorcore(M, K, N)
+
+        if extra_flops > 2:
+            logger.info("batch_matmul pad_to_tensorcore skipped, extra_flops %s", extra_flops)
+            return None
+
+        logger.info("batch_matmul pad_to_tensorcore, extra_flops %s", extra_flops)
+        if dm or dk:
+            x_ = relay.nn.pad(x, pad_width=((0, 0), (0, dm), (0, dk)))
+        else:
+            x_ = x
+        if dn or dk:
+            y_ = relay.nn.pad(y, pad_width=((0, 0), (0, dn), (0, dk)))
+        else:
+            y_ = y
+        out_ = relay.nn.batch_matmul(x_, y_)
+        if dm or dn:
+            original_out_shape = [x.value for x in output_tensor.shape]
+            out = relay.strided_slice(out_, begin=[0, 0, 0], end=original_out_shape)
+        else:
+            out = out_
+        return out
+    return None
+
+
+@nn.dense_legalize.register("cuda")
+def _dense_legalize(attrs, inputs, arg_types):
+    """Legalizes dense op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current convolution
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # Collect the input tensors.
+    x_tensor, y_tensor = arg_types[0], arg_types[1]
+    dtype = x_tensor.dtype
+
+    # Collect the output tensor.
+    output_tensor = arg_types[2]
+
+    # Collect the input exprs.
+    x, y = inputs
+
+    # Pad input and output channels to use tensorcore schedule.
+    if dtype in ["float16"]:  # todo: support int8/int4
+        M, K = x_tensor.shape
+        N, K = y_tensor.shape
+        try:
+            M = M.value
+            K = K.value
+            N = N.value
+        except AttributeError:
+            # todo: deal with unfixed shape when compiling wdl model
+            return None
+
+        # The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16, 8) or (8, 16, 32)
+        if (
+            (M % 8 == 0 and K % 16 == 0 and N % 32 == 0)
+            or (M % 16 == 0 and K % 16 == 0 and N % 16 == 0)
+            or (M % 32 == 0 and K % 16 == 0 and N % 8 == 0)
+        ):
+            # no need to pad
+            return None
+
+        (dm, dk, dn), extra_flops_ratio = pad_to_tensorcore(M, K, N)
+
+        if extra_flops_ratio > 2:
+            logger.info("dense pad_to_tensorcore skipped, extra_flops_ratio %s", extra_flops_ratio)
+            return None
+
+        logger.info("dense pad_to_tensorcore, extra_flops_ratio %s", extra_flops_ratio)
+
+        if dm or dk:
+            x_ = relay.nn.pad(x, pad_width=((0, dm), (0, dk)))
+        else:
+            x_ = x
+        if dn or dk:
+            y_ = relay.nn.pad(y, pad_width=((0, dn), (0, dk)))
+        else:
+            y_ = y
+        out_ = relay.nn.dense(x_, y_)
+        if dm or dn:
+            original_out_shape = [x.value for x in output_tensor.shape]
+            out = relay.strided_slice(out_, begin=[0, 0], end=original_out_shape)
+        else:
+            out = out_
+        return out
+    return None
+
+
+def pad_to_tensorcore(M, K, N):
+    """pad shape to enable tensorcore"""
+    candidates = [(16, 16, 16), (32, 16, 8), (8, 16, 32)]
+
+    flops = M * K * N
+    extra_flops = math.inf
+    best_pad = (0, 0, 0)
+    for padding in candidates:
+        dm, dk, dn = _pad_to(M, K, N, padding)
+        e = (M + dm) * (N + dn) * (K + dk) - M * N * K
+        # print(dm, dk, dn, e, flops)
+        if e < extra_flops:
+            extra_flops = e
+            best_pad = (dm, dk, dn)
+    return best_pad, extra_flops / flops
+
+
+def _pad_to(M, K, N, PADDING):
+    dm, dk, dn = 0, 0, 0
+
+    if M % PADDING[0] != 0:
+        M_ = ((M + PADDING[0]) // PADDING[0]) * PADDING[0]
+        dm = M_ - M
+    if K % PADDING[1] != 0:
+        K_ = ((K + PADDING[1]) // PADDING[1]) * PADDING[1]
+        dk = K_ - K
+    if N % PADDING[2] != 0:
+        N_ = ((N + PADDING[2]) // PADDING[2]) * PADDING[2]
+        dn = N_ - N
+
+    return dm, dk, dn

python/tvm/topi/nn/batch_matmul.py

@@ -16,6 +16,7 @@
 # under the License.
 """Batch matrix multiplication"""
 # pylint: disable=invalid-name
+import tvm
 from tvm import te, auto_scheduler
 from ..utils import get_const_tuple
 
@@ -77,3 +78,26 @@ def batch_matmul(x, y, oshape=None, auto_scheduler_rewritten_layout=""):
         output = auto_scheduler.rewrite_compute_body(output, auto_scheduler_rewritten_layout)
 
     return output
+
+
+@tvm.target.generic_func
+def batch_matmul_legalize(attrs, inputs, types):
+    """Legalizes batch_matmul op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current batch_matmul
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # not to change by default
+    # pylint: disable=unused-argument
+    return None

python/tvm/topi/nn/dense.py

@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 """TVM operator fully connected compute."""
+import tvm
 from tvm import te, auto_scheduler
 from .. import tag
 
@@ -80,3 +81,26 @@ def dense(data, weight, bias=None, out_dtype=None, auto_scheduler_rewritten_layo
         matmul = auto_scheduler.rewrite_compute_body(matmul, auto_scheduler_rewritten_layout)
 
     return matmul
+
+
+@tvm.target.generic_func
+def dense_legalize(attrs, inputs, types):
+    """Legalizes dense op.
+
+    Parameters
+    ----------
+    attrs : tvm.ir.Attrs
+        Attributes of current dense
+    inputs : list of tvm.relay.Expr
+        The args of the Relay expr to be legalized
+    types : list of types
+        List of input and output types
+
+    Returns
+    -------
+    result : tvm.relay.Expr
+        The legalized expr
+    """
+    # not to change by default
+    # pylint: disable=unused-argument
+    return None