jcf94 commented on a change in pull request #7146:
URL: https://github.com/apache/tvm/pull/7146#discussion_r547085365
##########
File path: python/tvm/relay/op/strategy/cuda.py
##########
@@ -657,6 +657,20 @@ def batch_matmul_strategy_cuda(attrs, inputs, out_type,
target):
name="batch_matmul_cublas.cuda",
plevel=15,
)
+ if target.kind.name == "cuda" and
nvcc.have_tensorcore(tvm.gpu(0).compute_version):
+ x, y = inputs
+ B, M, K = get_const_tuple(x.shape)
+ B, N, K = get_const_tuple(y.shape)
+ # "The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16,
8) or (8, 16, 32) for now"
+ 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)):
Review comment:
Will it be better to also add data type check here or use some other
user defined options?
TensorCore needs to be computed in float16, but I'm not sure if this will
bring any loss in precision if we just try to transform all float32
batch_matmul ops to compute in lower precision.
Besides, TensorCore can also support datatype like int8 in some higher cuda
versions.
##########
File path: python/tvm/topi/cuda/batch_matmul_tensorcore.py
##########
@@ -0,0 +1,274 @@
+# 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,too-many-locals,unused-variable,unused-argument
+"""cuda batch_matmul operators"""
+import tvm
+from tvm import autotvm
+from tvm import te
+from ..utils import traverse_inline, get_const_tuple
+from .tensor_intrin import intrin_wmma_load_matrix_A, \
+ intrin_wmma_load_matrix_W, intrin_wmma_store_matrix, intrin_wmma_gemm
+
[email protected]_topi_compute("batch_matmul_tensorcore.cuda")
+def batch_matmul_tensorcore(cfg, x, y, out_shape=None):
+ """batch matmul tensorcore operator on cuda"""
+ # todo: deal with out_shape for broadcast, liuxin.ai
+ return batch_matmul_tensorcore_cuda(x, y)
+
+
[email protected]_topi_schedule("batch_matmul_tensorcore.cuda")
+def schedule_batch_matmul_tensorcore(cfg, outs):
+ """Schedule for batch_matmul operator using Tensorcore
+
+ Parameters
+ ----------
+ outs: Array of Tensor
+ The computation graph description of batch_matmul
+ in the format of an array of tensors.
+
+ Returns
+ -------
+ s: Schedule
+ The computation schedule for the op.
+ """
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+
+ def _schedule(cfg, s, C):
+ A, B = s[C].op.input_tensors
+ batch, m_dim, k_dim = get_const_tuple(A.shape)
+ batch, n_dim, k_dim = get_const_tuple(B.shape)
+ out_dtype = C.dtype
+ # inline astype fp16
+ s[A].compute_inline()
+ s[B].compute_inline()
+
+ # Explicit memory access
+ AS = s.cache_read(A, 'shared', [C])
+ BS = s.cache_read(B, 'shared', [C])
+ AF = s.cache_read(AS, 'wmma.matrix_a', [C])
+ BF = s.cache_read(BS, 'wmma.matrix_b', [C])
+ CF = s.cache_write(C, 'wmma.accumulator')
+ CS = s.cache_read(CF, 'shared', [C])
+
+ # fallback support
+ target = tvm.target.Target.current()
+ if cfg.is_fallback:
+ ref_log = autotvm.tophub.load_reference_log(
+ target.kind.name, target.model, 'batch_matmul_tensorcore.cuda')
+ cfg.fallback_with_reference_log(ref_log)
+
+ # Deal with op fusion, such as bias/relu and slice after padding
+ if C.op not in s.outputs and "injective" in s.outputs[0].tag:
+ s[C].compute_inline()
+ C = s.outputs[0].output(0)
+
+ # create tuning space
+ cfg.define_knob("block_row_warps", [1, 2, 4])
+ cfg.define_knob("block_col_warps", [1, 2, 4])
+ cfg.define_knob("warp_row_tiles", [1, 2, 4])
+ cfg.define_knob("warp_col_tiles", [1, 2, 4])
+ cfg.define_knob("chunk", [1, 2, 4, 8])
+ cfg.define_knob("offset", [0, 8])
+ cfg.define_knob("offsetCS", [0, 8])
+ cfg.define_knob("vec", [1, 2, 4, 8])
+
+ # Ensure that the default parameters are applicable when autotvm is
not in use
+ if (m_dim % 32 == 0 and n_dim % 8 == 0):
+ cfg.define_knob("wmma_m", [32, 16, 8])
+ elif (m_dim % 16 == 0 and n_dim % 16 == 0):
+ cfg.define_knob("wmma_m", [16, 8, 32])
+ elif (m_dim % 8 == 0 and n_dim % 32 == 0):
+ cfg.define_knob("wmma_m", [8, 16, 32])
+
+ warp_size = 32
+ wmma_k = 16
+ block_row_warps = cfg["block_row_warps"].val
+ block_col_warps = cfg["block_col_warps"].val
+ warp_row_tiles = cfg["warp_row_tiles"].val
+ warp_col_tiles = cfg["warp_col_tiles"].val
+ chunk = cfg["chunk"].val
+ offset = cfg["offset"].val
+ offsetCS = cfg["offsetCS"].val
+ wmma_m = cfg["wmma_m"].val
+ vec = cfg["vec"].val
+
+ if wmma_m == 16:
+ wmma_n = 16
+ elif wmma_m == 8:
+ wmma_n = 32
+ elif wmma_m == 32:
+ wmma_n = 8
+
+ # Define the stride of intrin functions
+ AS_align = chunk * wmma_k + offset
+ BS_align = chunk * wmma_k + offset
+ CS_align = warp_col_tiles * block_col_warps * wmma_n + offsetCS
+ AS_stride = [AS_align, 1]
+ BS_stride = [BS_align, 1]
+ AF_stride = [wmma_k, 1]
+ BF_stride = [wmma_k, 1]
+ CF_stride = [warp_col_tiles * wmma_n, 1]
+ CS_stride = [CS_align, 1]
+
+ block_x = te.thread_axis('blockIdx.x')
+ block_y = te.thread_axis('blockIdx.y')
+ block_z = te.thread_axis('blockIdx.z')
+ thread_x = te.thread_axis('threadIdx.x')
+ thread_y = te.thread_axis('threadIdx.y')
+ thread_z = te.thread_axis('threadIdx.z')
+
+ # Schedule for dense computation
+ block_factor_m = wmma_m * warp_row_tiles * block_row_warps
+ block_factor_n = wmma_n * warp_col_tiles * block_col_warps
+ b, m, n = C.op.axis
+ block_i, bc = s[C].split(m, factor=block_factor_m)
+ block_j, oc = s[C].split(n, factor=block_factor_n)
+ s[C].reorder(b, block_i, block_j, bc, oc)
+ t = s[C].fuse(bc, oc)
+ t, vi = s[C].split(t, factor=vec)
+ t, tx = s[C].split(t, factor=warp_size)
+ t, ty = s[C].split(t, factor=block_row_warps)
+ t, tz = s[C].split(t, factor=block_col_warps)
+ s[C].bind(block_i, block_x)
+ s[C].bind(block_j, block_y)
+ s[C].bind(b, block_z)
+ s[C].bind(tz, thread_z)
+ s[C].bind(ty, thread_y)
+ s[C].bind(tx, thread_x)
+ s[C].vectorize(vi)
+
+ # Schedule for wmma store
+ s[CS].compute_at(s[C], block_j)
+ bs, bb, oo = CS.op.axis
+ s[CS].storage_align(bb, CS_align - 1, CS_align)
+ bb, bbi = s[CS].split(bb, factor=wmma_m)
+ oo, ooi = s[CS].split(oo, factor=wmma_n)
+ bb, bbii = s[CS].split(bb, factor=warp_row_tiles)
+ oo, ooii = s[CS].split(oo, factor=warp_col_tiles)
+ s[CS].reorder(bs, bb, oo, bbii, ooii, bbi, ooi)
+
+ # Schedule for wmma computation
+ s[CF].compute_at(s[CS], oo)
+ bs, warp_i, warp_j = CF.op.axis
+ warp_i, _ii = s[CF].split(warp_i, factor=wmma_m)
+ warp_j, _jj = s[CF].split(warp_j, factor=wmma_n)
+ k, = CF.op.reduce_axis
+ k, _k = s[CF].split(k, factor=wmma_k)
+ ko, ki = s[CF].split(k, factor=chunk)
+ s[CF].reorder(bs, ko, ki, warp_i, warp_j, _ii, _jj, _k)
+
+ # Schedule for wmma_matrix_a load
+ s[AF].compute_at(s[CF], ki)
+ bs, b, i = AF.op.axis
+ b, b_ii = s[AF].split(b, factor=wmma_m)
+ i, i_jj = s[AF].split(i, factor=wmma_k)
+ s[AF].reorder(bs, b, i, b_ii, i_jj)
+
+ # Schedule for wmma_matrix_b load
+ s[BF].compute_at(s[CF], ki)
+ bs, o, i = BF.op.axis
+ o, o_ii = s[BF].split(o, factor=wmma_n)
+ i, i_ii = s[BF].split(i, factor=wmma_k)
+ s[BF].reorder(bs, o, i, o_ii, i_ii)
+
+ # Schedule for A's(B's) shared memory load
+ def shared_shedule(stage, strides):
+ s[stage].compute_at(s[CF], ko)
+ bs, xo, yo = stage.op.axis
+ s[stage].storage_align(xo, strides - 1, strides)
+ t = s[stage].fuse(xo, yo)
+ t, vi = s[stage].split(t, factor=vec)
+ t, tx = s[stage].split(t, factor=warp_size)
+ t, ty = s[stage].split(t, factor=block_row_warps)
+ _, tz = s[stage].split(t, factor=block_col_warps)
+ s[stage].bind(ty, thread_y)
+ s[stage].bind(tz, thread_z)
+ s[stage].bind(tx, thread_x)
+ s[stage].vectorize(vi)
+
+ shared_shedule(AS, AS_align)
+ shared_shedule(BS, BS_align)
+
+ shape = (wmma_m, wmma_n, wmma_k)
+ in_dtype = 'float16'
Review comment:
Same concerns about the data type as above.
It's fine for this PR, but will be better to add more check or just put some
comments saying that the TensorCore needs to use a special data type, then if
some one meets any trouble, they can know how to check.
##########
File path: python/tvm/topi/cuda/batch_matmul_tensorcore.py
##########
@@ -0,0 +1,274 @@
+# 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,too-many-locals,unused-variable,unused-argument
+"""cuda batch_matmul operators"""
+import tvm
+from tvm import autotvm
+from tvm import te
+from ..utils import traverse_inline, get_const_tuple
+from .tensor_intrin import intrin_wmma_load_matrix_A, \
+ intrin_wmma_load_matrix_W, intrin_wmma_store_matrix, intrin_wmma_gemm
+
[email protected]_topi_compute("batch_matmul_tensorcore.cuda")
+def batch_matmul_tensorcore(cfg, x, y, out_shape=None):
+ """batch matmul tensorcore operator on cuda"""
+ # todo: deal with out_shape for broadcast, liuxin.ai
+ return batch_matmul_tensorcore_cuda(x, y)
+
+
[email protected]_topi_schedule("batch_matmul_tensorcore.cuda")
+def schedule_batch_matmul_tensorcore(cfg, outs):
+ """Schedule for batch_matmul operator using Tensorcore
+
+ Parameters
+ ----------
+ outs: Array of Tensor
+ The computation graph description of batch_matmul
+ in the format of an array of tensors.
+
+ Returns
+ -------
+ s: Schedule
+ The computation schedule for the op.
+ """
+ outs = [outs] if isinstance(outs, te.tensor.Tensor) else outs
+ s = te.create_schedule([x.op for x in outs])
+
+ def _schedule(cfg, s, C):
+ A, B = s[C].op.input_tensors
+ batch, m_dim, k_dim = get_const_tuple(A.shape)
+ batch, n_dim, k_dim = get_const_tuple(B.shape)
+ out_dtype = C.dtype
+ # inline astype fp16
+ s[A].compute_inline()
+ s[B].compute_inline()
+
+ # Explicit memory access
+ AS = s.cache_read(A, 'shared', [C])
+ BS = s.cache_read(B, 'shared', [C])
+ AF = s.cache_read(AS, 'wmma.matrix_a', [C])
+ BF = s.cache_read(BS, 'wmma.matrix_b', [C])
+ CF = s.cache_write(C, 'wmma.accumulator')
+ CS = s.cache_read(CF, 'shared', [C])
+
+ # fallback support
+ target = tvm.target.Target.current()
+ if cfg.is_fallback:
+ ref_log = autotvm.tophub.load_reference_log(
+ target.kind.name, target.model, 'batch_matmul_tensorcore.cuda')
+ cfg.fallback_with_reference_log(ref_log)
+
+ # Deal with op fusion, such as bias/relu and slice after padding
+ if C.op not in s.outputs and "injective" in s.outputs[0].tag:
+ s[C].compute_inline()
+ C = s.outputs[0].output(0)
+
+ # create tuning space
+ cfg.define_knob("block_row_warps", [1, 2, 4])
+ cfg.define_knob("block_col_warps", [1, 2, 4])
+ cfg.define_knob("warp_row_tiles", [1, 2, 4])
+ cfg.define_knob("warp_col_tiles", [1, 2, 4])
+ cfg.define_knob("chunk", [1, 2, 4, 8])
+ cfg.define_knob("offset", [0, 8])
+ cfg.define_knob("offsetCS", [0, 8])
+ cfg.define_knob("vec", [1, 2, 4, 8])
+
+ # Ensure that the default parameters are applicable when autotvm is
not in use
+ if (m_dim % 32 == 0 and n_dim % 8 == 0):
+ cfg.define_knob("wmma_m", [32, 16, 8])
+ elif (m_dim % 16 == 0 and n_dim % 16 == 0):
+ cfg.define_knob("wmma_m", [16, 8, 32])
+ elif (m_dim % 8 == 0 and n_dim % 32 == 0):
+ cfg.define_knob("wmma_m", [8, 16, 32])
+
+ warp_size = 32
+ wmma_k = 16
+ block_row_warps = cfg["block_row_warps"].val
+ block_col_warps = cfg["block_col_warps"].val
+ warp_row_tiles = cfg["warp_row_tiles"].val
+ warp_col_tiles = cfg["warp_col_tiles"].val
+ chunk = cfg["chunk"].val
+ offset = cfg["offset"].val
+ offsetCS = cfg["offsetCS"].val
+ wmma_m = cfg["wmma_m"].val
+ vec = cfg["vec"].val
+
+ if wmma_m == 16:
+ wmma_n = 16
+ elif wmma_m == 8:
+ wmma_n = 32
+ elif wmma_m == 32:
+ wmma_n = 8
+
+ # Define the stride of intrin functions
+ AS_align = chunk * wmma_k + offset
+ BS_align = chunk * wmma_k + offset
+ CS_align = warp_col_tiles * block_col_warps * wmma_n + offsetCS
+ AS_stride = [AS_align, 1]
+ BS_stride = [BS_align, 1]
+ AF_stride = [wmma_k, 1]
+ BF_stride = [wmma_k, 1]
+ CF_stride = [warp_col_tiles * wmma_n, 1]
+ CS_stride = [CS_align, 1]
+
+ block_x = te.thread_axis('blockIdx.x')
+ block_y = te.thread_axis('blockIdx.y')
+ block_z = te.thread_axis('blockIdx.z')
+ thread_x = te.thread_axis('threadIdx.x')
+ thread_y = te.thread_axis('threadIdx.y')
+ thread_z = te.thread_axis('threadIdx.z')
+
+ # Schedule for dense computation
+ block_factor_m = wmma_m * warp_row_tiles * block_row_warps
+ block_factor_n = wmma_n * warp_col_tiles * block_col_warps
+ b, m, n = C.op.axis
+ block_i, bc = s[C].split(m, factor=block_factor_m)
+ block_j, oc = s[C].split(n, factor=block_factor_n)
+ s[C].reorder(b, block_i, block_j, bc, oc)
+ t = s[C].fuse(bc, oc)
+ t, vi = s[C].split(t, factor=vec)
+ t, tx = s[C].split(t, factor=warp_size)
+ t, ty = s[C].split(t, factor=block_row_warps)
+ t, tz = s[C].split(t, factor=block_col_warps)
+ s[C].bind(block_i, block_x)
+ s[C].bind(block_j, block_y)
+ s[C].bind(b, block_z)
+ s[C].bind(tz, thread_z)
+ s[C].bind(ty, thread_y)
+ s[C].bind(tx, thread_x)
+ s[C].vectorize(vi)
+
+ # Schedule for wmma store
+ s[CS].compute_at(s[C], block_j)
+ bs, bb, oo = CS.op.axis
+ s[CS].storage_align(bb, CS_align - 1, CS_align)
+ bb, bbi = s[CS].split(bb, factor=wmma_m)
+ oo, ooi = s[CS].split(oo, factor=wmma_n)
+ bb, bbii = s[CS].split(bb, factor=warp_row_tiles)
+ oo, ooii = s[CS].split(oo, factor=warp_col_tiles)
+ s[CS].reorder(bs, bb, oo, bbii, ooii, bbi, ooi)
+
+ # Schedule for wmma computation
+ s[CF].compute_at(s[CS], oo)
+ bs, warp_i, warp_j = CF.op.axis
+ warp_i, _ii = s[CF].split(warp_i, factor=wmma_m)
+ warp_j, _jj = s[CF].split(warp_j, factor=wmma_n)
+ k, = CF.op.reduce_axis
+ k, _k = s[CF].split(k, factor=wmma_k)
+ ko, ki = s[CF].split(k, factor=chunk)
+ s[CF].reorder(bs, ko, ki, warp_i, warp_j, _ii, _jj, _k)
+
+ # Schedule for wmma_matrix_a load
+ s[AF].compute_at(s[CF], ki)
+ bs, b, i = AF.op.axis
+ b, b_ii = s[AF].split(b, factor=wmma_m)
+ i, i_jj = s[AF].split(i, factor=wmma_k)
+ s[AF].reorder(bs, b, i, b_ii, i_jj)
+
+ # Schedule for wmma_matrix_b load
+ s[BF].compute_at(s[CF], ki)
+ bs, o, i = BF.op.axis
+ o, o_ii = s[BF].split(o, factor=wmma_n)
+ i, i_ii = s[BF].split(i, factor=wmma_k)
+ s[BF].reorder(bs, o, i, o_ii, i_ii)
+
+ # Schedule for A's(B's) shared memory load
+ def shared_shedule(stage, strides):
+ s[stage].compute_at(s[CF], ko)
+ bs, xo, yo = stage.op.axis
+ s[stage].storage_align(xo, strides - 1, strides)
+ t = s[stage].fuse(xo, yo)
+ t, vi = s[stage].split(t, factor=vec)
+ t, tx = s[stage].split(t, factor=warp_size)
+ t, ty = s[stage].split(t, factor=block_row_warps)
+ _, tz = s[stage].split(t, factor=block_col_warps)
+ s[stage].bind(ty, thread_y)
+ s[stage].bind(tz, thread_z)
+ s[stage].bind(tx, thread_x)
+ s[stage].vectorize(vi)
+
+ shared_shedule(AS, AS_align)
+ shared_shedule(BS, BS_align)
+
+ shape = (wmma_m, wmma_n, wmma_k)
+ in_dtype = 'float16'
+ AL_gemm = te.placeholder((wmma_m, wmma_k), name='AL_gemm',
dtype=in_dtype)
+ BL_gemm = te.placeholder((wmma_n, wmma_k), name='BL_gemm',
dtype=in_dtype)
+ k_gemm = te.reduce_axis((0, wmma_k), name='k_gemm')
+ CL_compute = te.compute((wmma_m, wmma_n), lambda ii, jj:
+ te.sum(AL_gemm[ii, k_gemm].astype(out_dtype) * \
+ BL_gemm[jj, k_gemm].astype(out_dtype), \
+ axis=k_gemm), name='CL_compute')
+
+ # lower the computation loops down to TensorCore hardware intrinsics
+ # by mapping the dense tensorcore to tensor intrinsics
+ s[AF].tensorize(b_ii, intrin_wmma_load_matrix_A( \
+ AF_stride, AS_stride, shape, "row_major", \
+ (wmma_m, wmma_k), (wmma_m, wmma_k), 'float16'))
+ s[BF].tensorize(o_ii, intrin_wmma_load_matrix_W( \
+ BF_stride, BS_stride, shape, "col_major", \
+ (wmma_n, wmma_k), (wmma_n, wmma_k), 'float16'))
+ s[CF].tensorize(_ii, intrin_wmma_gemm( \
+ AL_gemm, BL_gemm, CL_compute, AF_stride, BF_stride, CF_stride,
shape))
+ s[CS].tensorize(bbi, intrin_wmma_store_matrix( \
+ CS_stride, CF_stride, shape, out_dtype, (wmma_m, wmma_n), (wmma_m,
wmma_n)))
+
+ def _callback(op):
+ if "batch_matmul_tensorcore" in op.tag:
+ _schedule(cfg, s, op.output(0))
+
+ traverse_inline(s, outs[0].op, _callback)
+ return s
+
+def batch_matmul_tensorcore_cuda(x, y):
+ """Computes batch matrix multiplication of `x` and `y` when `x` and `y` are
+ data in batch.
+
+ Parameters
+ ----------
+ x : tvm.te.Tensor
+ 3-D with shape [batch, M, K]
+
+ y : tvm.te.Tensor
+ 3-D with shape [batch, N, K]
+
+ Returns
+ -------
+ output : tvm.te.Tensor
+ 3-D with shape [batch, M, N]
+ """
+ assert len(x.shape) == 3 and len(y.shape) == 3, "only support 3-dim
batch_matmul"
+ x_shape = get_const_tuple(x.shape)
+ y_shape = get_const_tuple(y.shape)
+ assert x_shape[0] == y_shape[0], "batch dimension doesn't match"
+ assert x_shape[2] == y_shape[2], "shapes of x and y is inconsistant"
+ batch, M, K = x.shape
+ N = y.shape[1]
+ out_dtype = x.dtype
+
+ assert ((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)), \
+ "The shape of (M, K, N) must be multiple of (16, 16, 16) or (32, 16,
8) or (8, 16, 32) for now"
+
+ x_16 = te.compute((batch, M, K), lambda b, i, k: x[b, i,
k].astype('float16'))
+ y_16 = te.compute((batch, N, K), lambda b, j, k: y[b, j,
k].astype('float16'))
Review comment:
ditto
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