[TOPI] Add dense schedule for fp16 and fp32 using gemm

python/tvm/relay/op/strategy/arm_cpu.py

@@ -729,6 +729,18 @@ def schedule_dense_arm_cpu(attrs, inputs, out_type, target):
             plevel=12,
         )
 
+    if (
+        target.features.is_aarch64
+        and data.dtype in ["float16", "float32"]
+        and weight.dtype in ["float16", "float32"]
+        and out_type.dtype in ["float16", "float32"]
+    ):
+        strategy.add_implementation(
+            wrap_compute_dense(topi.arm_cpu.dense_gemm),
+            wrap_topi_schedule(topi.arm_cpu.schedule_dense_gemm),
+            name="dense_gemm.arm_cpu",
+            plevel=11,
+        )
     # Fallback to x86 schedules as there is currently no arm_cpu schedule for dense
     strategy.add_implementation(
         wrap_compute_dense(topi.x86.dense_nopack),
@@ -773,6 +785,19 @@ def matmul_strategy_arm_cpu(attrs, inputs, out_type, target):
             lambda: None,
             name="matmul.arm_cpu.sme",
         )
+    elif (
+        target.features.is_aarch64
+        and data.dtype in ["float16", "float32"]
+        and weight.dtype in ["float16", "float32"]
+        and out_type.dtype in ["float16", "float32"]
+        and not (attrs.transpose_a or attrs.transpose_b)
+        and len(data.shape) == 2
+    ):
+        strategy.add_implementation(
+            wrap_compute_matmul(topi.arm_cpu.dense_gemm),
+            wrap_topi_schedule(topi.arm_cpu.schedule_dense_gemm),
+            name="matmul.arm_cpu.neon",
+        )
         return strategy
 
     logger.warning("matmul is not optimized for arm cpu.")

python/tvm/testing/utils.py

@@ -871,6 +871,11 @@ def _multi_gpu_exists():
     "x86", "x86 Architecture", run_time_check=lambda: platform.machine() == "x86_64"
 )
 
+# Mark a test as requiring the aarch64 Architecture to run.
+requires_aarch64 = Feature(
+    "AArch64", "AArch64 Architecture", run_time_check=lambda: platform.machine() == "aarch64"
+)
+
 # Mark a test as requiring the CUDA runtime.
 requires_cuda = Feature(
     "cuda",

python/tvm/topi/arm_cpu/dense.py

@@ -16,20 +16,29 @@
 # under the License.
 """Dense schedule for ARM CPU"""
 from tvm import autotvm
-
-from .mprofile.dsp.dense import (
-    dense_dsp_schedule,
-    dense_dsp_compute,
-)
+from .mprofile.dsp.dense import dense_dsp_schedule, dense_dsp_compute
+from .dense_gemm import dense_gemm_compute, dense_gemm_schedule
 
 
 @autotvm.register_topi_compute("dense_dsp.arm_cpu")
 def dense_dsp(cfg, data, weight, bias, out_dtype):
-    """Compute dense_dsp with v7e-m DSP instructions."""
+    """Compute dense with DSP instructions."""
     return dense_dsp_compute(cfg, data, weight, bias=bias, out_dtype=out_dtype)
 
 
 @autotvm.register_topi_schedule("dense_dsp.arm_cpu")
 def schedule_dense_dsp(cfg, outs):
     """Create schedule for dense_dsp"""
     return dense_dsp_schedule(cfg, outs)
+
+
+@autotvm.register_topi_compute("dense_gemm.arm_cpu")
+def dense_gemm(cfg, data, weight, bias, out_dtype, transpose_a=False, transpose_b=True):
+    """Compute dense using GeMM."""
+    return dense_gemm_compute(cfg, data, weight, bias, out_dtype, transpose_a, transpose_b)
+
+
+@autotvm.register_topi_schedule("dense_gemm.arm_cpu")
+def schedule_dense_gemm(cfg, outs):
+    """Create schedule for dense using GeMM."""
+    return dense_gemm_schedule(cfg, outs)

python/tvm/topi/arm_cpu/dense_alter_op.py

@@ -15,6 +15,7 @@
 # specific language governing permissions and limitations
 # under the License.
 
+# pylint: disable=invalid-name,unused-variable,unused-argument,no-member
 """Dense alter op definitions for the `arm_cpu` device key."""
 
 import tvm
@@ -47,13 +48,11 @@ def _alter_dense(attrs, inputs, tinfos, out_type):
 
     cfg = dispatch_ctx.query(target, workload)
     topi_impl = workload[0]
+
     if topi_impl == "matmul.arm_cpu.sme":
-        # Pre-compute transposed weights and convert to a matmul
-        assert isinstance(
-            inputs[1], relay.Constant
-        ), "matmul_sme.arm_cpu requires weights be a Relay Constant"
 
         weight_dtype = tinfos[1].dtype
+        N, K = tinfos[1].shape
         encoded_weight = inputs[1]
 
         # For dense the weights (rhs) are provided in transposed format,
@@ -65,15 +64,15 @@ def _alter_dense(attrs, inputs, tinfos, out_type):
         # float16->float32 schedule the transformation currently happens at runtime
         # with the ARM_SME_BLOCK2_2SVLx1SVL_FP16_TRANSPOSE_INTERLEAVE intrinsic.
         if weight_dtype == "float32":
-            encoded_weight = relay.const(encoded_weight.data.numpy().transpose(), weight_dtype)
+            encoded_weight = relay.transpose(encoded_weight)
             transpose_b = False
 
-        new_weight = te.placeholder((encoded_weight.data.shape), dtype=weight_dtype)
+        new_weight = te.placeholder(([K, N]), dtype=weight_dtype)
+
         new_workload = autotvm.task.args_to_workload(
             [tinfos[0], new_weight, None, out_type.dtype, False, transpose_b], topi_impl
         )
         dispatch_ctx.update(target, new_workload, cfg)
-
         return _make.matmul(
             inputs[0],
             encoded_weight,
@@ -82,6 +81,27 @@ def _alter_dense(attrs, inputs, tinfos, out_type):
             False,
             transpose_b,
         )
+    elif topi_impl == "dense_gemm.arm_cpu":
+
+        weight_dtype = tinfos[1].dtype
+        N, K = tinfos[1].shape
+
+        encoded_weight = relay.transpose(inputs[1])
+        new_weight = te.placeholder(([K, N]), dtype=weight_dtype)
+
+        new_workload = autotvm.task.args_to_workload(
+            [tinfos[0], new_weight, None, out_type.dtype, False, False], topi_impl
+        )
+        dispatch_ctx.update(target, new_workload, cfg)
+
+        return _make.matmul(
+            inputs[0],
+            encoded_weight,
+            attrs.units,
+            attrs.out_dtype,
+            False,
+            False,
+        )
 
     # x86 schedules are used as a fallback
     return tvm.topi.x86.dense_alter_op._alter_dense_layout(attrs, inputs, tinfos, out_type)

python/tvm/topi/arm_cpu/dense_gemm.py

@@ -0,0 +1,174 @@
+# 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, too-many-locals
+# pylint: disable=unused-argument, redefined-builtin
+"""GeMM dense schedule on AArch64"""
+import tvm
+from tvm import te
+from tvm.topi import nn
+from tvm.topi.arm_cpu.arm_utils import get_tiling_A, get_tiling_B_transformed, pad_dim_to_multiple
+from ..utils import get_const_tuple, traverse_inline
+from .. import tag
+
+# Compute function
+def dense_gemm_compute(
+    cfg, data, weight, bias=None, out_dtype=None, transpose_a=False, transpose_b=True
+):
+    """
+    Compute dense using GeMM.
+
+    Parameters
+    ----------
+    cfg : Autotvm tuning space config file,
+        empty in this case, but it's needed as an arg.
+
+    data : tvm.te.Tensor
+        2-D with shape [M, K] or [K, M].
+
+    weight : tvm.te.Tensor
+        2-D with shape [K, N] or [N, K].
+
+    bias : Optional[tvm.te.Tensor]
+        1-D with shape [N]
+
+
+    out_dtype : Optional[str]
+        Specifies the output data type.
+
+    transpose_a : Optional[bool] = False
+    Whether the data tensor is in transposed format.
+
+    transpose_b : Optional[bool] = True
+    Whether the weight tensor is in transposed format.
+
+    Returns
+    -------
+    out : tvm.te.Tensor
+        1-D with shape [out_dim]
+    """
+
+    if out_dtype is None:
+        out_dtype = data.dtype
+    M, K = get_const_tuple(data.shape)  # batch, in_dim
+    if bool(transpose_b):  # out_dim
+        (N, _) = get_const_tuple(weight.shape)
+    else:
+        (_, N) = get_const_tuple(weight.shape)
+
+    tile_M, tile_K = get_tiling_A(False, out_dtype)
+    tile_N, _ = get_tiling_B_transformed(False, out_dtype, False)
+
+    M_padded, pad_M = pad_dim_to_multiple(M, tile_M)
+    K_padded, pad_K = pad_dim_to_multiple(K, tile_K)
+    N_padded, pad_N = pad_dim_to_multiple(N, tile_N)
+    m_pad_after = (pad_M, pad_K)
+    n_pad_after = (pad_N, pad_K) if transpose_b else (pad_K, pad_N)
+
+    if pad_M != 0 or pad_K != 0:
+        data = nn.pad(data, pad_before=(0, 0), pad_after=m_pad_after, name="data_padded")
+
+    k = te.reduce_axis((0, K_padded), name="k")
+
+    if bool(transpose_b):
+        weight = te.compute(
+            (K_padded, N_padded), lambda x, y: weight[y, x], name="weight_transposed"
+        )
+
+    if pad_N != 0 or pad_K != 0:
+        weight = nn.pad(weight, pad_before=(0, 0), pad_after=n_pad_after, name="weight_padded")
+
+    C = te.compute(
+        (M_padded, N_padded),
+        lambda x, y: te.sum(
+            data[x, k].astype(out_dtype) * weight[k, y].astype(out_dtype),
+            axis=k,
+        ).astype(out_dtype),
+        name="C",
+    )
+
+    if bias is not None:
+        C = te.compute(
+            (M_padded, N_padded),
+            lambda i, j: C[i, j] + bias[j].astype(out_dtype),
+            tag=tag.BROADCAST,
+            name="dense_biased_output",
+        )
+
+    # We need to ensure that infer bound pass does not remove the padding
+    # which is necessary for the tensorizations to work. So we need to
+    # add a dummy reference to the padding area of the result
+    zero = (
+        tvm.tir.const(1, C.dtype) * C[0, N_padded - 1]
+        - tvm.tir.const(1, C.dtype) * C[0, N_padded - 1]
+    )
+
+    out = te.compute(
+        (M, N), lambda x, y: (C[x, y] + zero).astype(out_dtype), name="dense_gemm_output"
+    )
+
+    return out
+
+
+def _dense_gemm_schedule(s, out):
+    C = out.op.input_tensors[0]
+    A = C.op.input_tensors[0]
+    out_type = A.dtype
+    tile_M, tile_K = get_tiling_A(False, out_type)
+    tile_N, _ = get_tiling_B_transformed(False, out_type, False)
+
+    if C.op.name == "dense_biased_output":
+        s[C].compute_inline()
+        C = C.op.input_tensors[0]
+    x, y = s[C].op.axis
+    (k,) = s[C].op.reduce_axis
+
+    k_outer, k_inner = s[C].split(k, factor=tile_K)
+    x_outer, x_inner = s[C].split(x, factor=tile_M)
+    y_outer, y_inner = s[C].split(y, factor=tile_N)
+    y_inner_outer, y_inner_inner = s[C].split(y_inner, nparts=4)
+    s[C].parallel(x_outer)
+    s[C].reorder(
+        x_outer,
+        y_outer,
+        k_outer,
+        k_inner,
+        y_inner_outer,
+        x_inner,
+        y_inner_inner,
+    )
+    s[C].unroll(y_inner_outer)
+    s[C].unroll(x_inner)
+    s[C].vectorize(y_inner_inner)
+
+    return s
+
+
+def dense_gemm_schedule(cfg, outs):
+    """Schedule the dense_gemm strategy"""
+    s = te.create_schedule([x.op for x in outs])
+    out = outs[0]
+    x, y = out.op.axis
+    _, inner = s[out].split(y, 4)
+    s[out].parallel(x)
+    s[out].vectorize(inner)
+
+    def _callback(op):
+        if "dense_gemm_output" in op.name:
+            _dense_gemm_schedule(s, op.output(0))
+
+    traverse_inline(s, out.op, _callback)
+    return s

python/tvm/topi/nn/dense.py

@@ -70,6 +70,7 @@ def matmul(
     assert (
         len(tensor_a.shape) >= 2 and len(tensor_b.shape) >= 2
     ), "1-dim matmul is not supported yet."
+
     if bias is not None:
         assert len(bias.shape) == 1
     if out_dtype is None:
@@ -229,6 +230,7 @@ def dense(
     output : tvm.te.Tensor
         2-D with shape [batch, out_dim]
     """
+
     return matmul(
         data,
         weight,

tests/python/frontend/keras/test_forward.py

@@ -93,7 +93,7 @@ def get_keras_output(in_data):
     def get_tvm_output(in_data, target, dev, dtype="float32"):
         shape_dict = {name: x.shape for (name, x) in zip(keras_model.input_names, in_data)}
         mod, params = relay.frontend.from_keras(keras_model, shape_dict, layout=layout)
-        with tvm.transform.PassContext(opt_level=2):
+        with tvm.transform.PassContext(opt_level=3):
             lib = relay.build(mod, target, params=params)
         m = graph_executor.GraphModule(lib["default"](dev))
         for name, x in zip(keras_model.input_names, in_data):