mlc-llm [Bug] 检查失败：(args.size() == initial_indices_orig.size()) 为 false

r7s23pms  于 5个月前  发布在  其他
关注(0)|答案(4)|浏览(108)

🐛 Bug

重现问题

重现问题的步骤：

  1. 我使用op conv2d构建了一个模型。
  2. 一个计算图是：permute dim--> conv2d-->layernorm。
  3. 在编译过程中，我遇到了以下问题。
    我认为这个问题是由于在dl.gpu.Matmul()之后融合了permute和conv操作符，导致缓冲区形状和index_map形状不匹配。

1、错误日志
tvm.error.InternalError: Traceback (most recent call last): 4: operator() at /workspace/tvm-unity/src/tir/schedule/schedule.cc:287 3: tvm::tir::TracedScheduleNode::TransformLayout(tvm::tir::BlockRV const&, int, tvm::tir::BufferIndexType, tvm::tir::IndexMap const&, tvm::runtime::Optional<tvm::tir::IndexMap> const&, bool) at /workspace/tvm-unity/src/tir/schedule/traced_schedule.cc:678 2: tvm::tir::ConcreteScheduleNode::TransformLayout(tvm::tir::BlockRV const&, int, tvm::tir::BufferIndexType, tvm::tir::IndexMap const&, tvm::runtime::Optional<tvm::tir::IndexMap> const&, bool) at /workspace/tvm-unity/src/tir/schedule/concrete_schedule.cc:993 1: tvm::tir::TransformLayout(tvm::tir::ScheduleState, tvm::tir::StmtSRef const&, int, tvm::tir::BufferIndexType, tvm::tir::IndexMap const&, tvm::runtime::Optional<tvm::tir::IndexMap> const&, bool) at /workspace/tvm-unity/src/tir/schedule/primitive/layout_transformation.cc:1160 0: tvm::tir::LegalizeIndexMapDType(tvm::tir::IndexMap const&, tvm::runtime::Array<tvm::PrimExpr, void> const&) at /workspace/tvm-unity/src/tir/schedule/primitive/layout_transformation.cc:1106 File "/workspace/tvm-unity/src/tir/schedule/primitive/layout_transformation.cc", line 1106 InternalError: Check failed: (args.size() == initial_indices_orig.size()) is false:

2、其他信息
1). T.index_map(lambda i0, i1, i2, i3, i4, i5: (T.int64(0), i1 * T.int64(64) + i2, i3)) ??? is not match?
2). with T.block("conv2d_nchw", no_realize=True): v_nn = T.axis.spatial(T.int64(1)) v_ff = T.axis.spatial(T.int64(256)) v_yy = T.axis.spatial(T.int64(64)) v_xx = T.axis.spatial(T.int64(64)) v_rc = T.axis.reduce(T.int64(768)) v_ry = T.axis.reduce(T.int64(1)) v_rx = T.axis.reduce(T.int64(1)) pad_temp = T.Buffer((T.int64(1), T.int64(768), T.int64(64), T.int64(64)), "float16") B = T.Buffer((T.int64(256), T.int64(768), T.int64(1), T.int64(1)), "float16") T.reads(pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx], B[v_ff, v_rc, v_ry, v_rx]) conv2d_nchw = T.Buffer((T.int64(1), T.int64(256), T.int64(64), T.int64(64)), "float16") T.writes(conv2d_nchw[v_nn, v_ff, v_yy, v_xx]) with T.init(): conv2d_nchw[v_nn, v_ff, v_yy, v_xx] = T.float16(0) conv2d_nchw[v_nn, v_ff, v_yy, v_xx] = conv2d_nchw[v_nn, v_ff, v_yy, v_xx] + pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx] * B[v_ff, v_rc, v_ry, v_rx]
3). @T.prim_func(private=True) def main(permute_dims161: T.Buffer((T.int64(1), T.int64(768), T.int64(64), T.int64(64)), "float16"), vision_tower_vision_tower_high_neck_0_weight1: T.Buffer((T.int64(256), T.int64(768), T.int64(1), T.int64(1)), "float16"), compute_intermediate: T.Buffer((T.int64(1), T.int64(256), T.int64(64), T.int64(64)), "float32")): T.func_attr({"tir.noalias": T.bool(True)}) # with T.block("root"): pad_temp = T.alloc_buffer((T.int64(1), T.int64(768), T.int64(64), T.int64(64)), "float16") conv2d_nchw_intermediate = T.alloc_buffer((T.int64(1), T.int64(256), T.int64(64), T.int64(64)), "float16") for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(768), T.int64(64), T.int64(64)): with T.block("pad_temp"): v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) T.reads(permute_dims161[v_i0, v_i1, v_i2, v_i3]) T.writes(pad_temp[v_i0, v_i1, v_i2, v_i3]) pad_temp[v_i0, v_i1, v_i2, v_i3] = permute_dims161[v_i0, v_i1, v_i2, v_i3] for nn, ff, yy, xx, rc, ry, rx in T.grid(T.int64(1), T.int64(256), T.int64(64), T.int64(64), T.int64(768), T.int64(1), T.int64(1)): with T.block("conv2d_nchw"): v_nn, v_ff, v_yy, v_xx, v_rc, v_ry, v_rx = T.axis.remap("SSSSRRR", [nn, ff, yy, xx, rc, ry, rx]) T.reads(pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx], vision_tower_vision_tower_high_neck_0_weight1[v_ff, v_rc, v_ry, v_rx]) T.writes(conv2d_nchw_intermediate[v_nn, v_ff, v_yy, v_xx]) with T.init(): conv2d_nchw_intermediate[v_nn, v_ff, v_yy, v_xx] = T.float16(0) conv2d_nchw_intermediate[v_nn, v_ff, v_yy, v_xx] = conv2d_nchw_intermediate[v_nn, v_ff, v_yy, v_xx] + pad_temp[v_nn, v_rc, v_yy + v_ry, v_xx + v_rx] * vision_tower_vision_tower_high_neck_0_weight1[v_ff, v_rc, v_ry, v_rx] for i0, i1, i2, i3 in T.grid(T.int64(1), T.int64(256), T.int64(64), T.int64(64)): with T.block("compute"): v_i0, v_i1, v_i2, v_i3 = T.axis.remap("SSSS", [i0, i1, i2, i3]) T.reads(conv2d_nchw_intermediate[v_i0, v_i1, v_i2, v_i3]) T.writes(compute_intermediate[v_i0, v_i1, v_i2, v_i3]) compute_intermediate[v_i0, v_i1, v_i2, v_i3] = T.Cast("float32", conv2d_nchw_intermediate[v_i0, v_i1, v_i2, v_i3])
T.index_map(lambda i0, i1, i2, i3, i4, i5: (T.int64(0), i1 * T.int64(64) + i2, i3))

mlc-llm

4条答案

按热度按时间
0kjbasz6

0kjbasz61#

感谢您的报告，如果能够获得最小重现，那将是有帮助的。
您可以在转换之前导出TVMScript,最小化它并运行您提到的转换。

赞(0）分享  回复(0）举报  5个月前
r1zhe5dt

r1zhe5dt2#

我遇到了相同的问题，你解决了吗？

赞(0）分享  回复(0）举报  5个月前
ijxebb2r

ijxebb2r3#

感谢报告，如果能得到最小重现，那将很有帮助。您可以在转换之前导出TVMScript,最小化它并运行您提到的转换。

嗨，这个bug可以像这样重现：

from tvm.relax.frontend import nn

class _Conv2d(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv = nn.modules.Conv2D(
            in_channels=768,
            out_channels=256,
            kernel_size=1,
            padding=0,
            bias=False
        )
    def forward(self, x: nn.Tensor):
        return self.conv(x)

from tvm.relax.frontend.nn import spec
forward_spec = {
    "forward": {
        "x": spec.Tensor([1, 768, 64, 64], dtype="float32")
    }
}
_conv2d_mod, params = _Conv2d().export_tvm(
    spec=forward_spec,
    debug=True
)

mod = _conv2d_mod

def _pipeline(mod):
    seq = tvm.transform.Sequential(
        [
            tvm.relax.transform.LegalizeOps(),
            tvm.relax.transform.AnnotateTIROpPattern(),
            tvm.relax.transform.FoldConstant(),
            tvm.relax.transform.FuseOps(),
            tvm.relax.transform.FuseTIR(),
            dl.ApplyDefaultSchedule(
                dl.gpu.Matmul(),
                dl.gpu.GEMV(),
                dl.gpu.Reduction(),
                dl.gpu.GeneralReduction(),
                dl.gpu.Fallback(),
            )
        ]
    )
    mod = seq(mod)
    return mod

with tvm.target.Target("nvidia/geforce-rtx-4090", host="llvm"):
    mod = _pipeline(mod)

mod.show()

只有在kernel_size等于1时，dl.gpu.Matmul 在 tvm.relax.transform.LegalizeOps() 之后才会报告错误。在MLLM模型中，可能涉及到图像嵌入，并且此操作(conv2d中的kernel_size等于1)可能会被使用。

赞(0）分享  回复(0）举报  5个月前
am46iovg

am46iovg4#

你好，@senlyu163。看起来在应用dlight到conv2d的内核大小为1时，这是一个已知的问题。这个问题的出现是因为reindex调度在expr上进行了简化。为了解决这个问题，我之前创建了一个草稿PR。你可以合并相关的更改并修改dlight的normalize_to_matmul函数。
检出这个草稿PR:apache/tvm#16440
与此问题相关的关键组件是向cache_reindex添加一个skip_simplify标志。你可以按照以下方式应用相关的更改：

def normalize_to_matmul(sch: tir.Schedule,
                        main_block: BlockRV,
                        layout: Optional[List[str]] = None) -> Optional[tir.Schedule]:
    if layout is None:
        layout = ["n", "t", "n"]
    block_stmt = sch.get(main_block)

    # Let layout be 'a' to auto infer the layout
    index_maps = get_index_map(block_stmt, layout=layout)
    if index_maps is None:
        logger.debug("Cannot find the appropriate index map for tensorcore")
        return None

    matmul_index_map, a_index_map, b_index_map, c_index_map = index_maps

    # Use `skip_simplify` to avoid the bug in the 1x1 conv
    block = sch.reindex(main_block, ("read", 0), skip_simplify=True)
    sch.transform_layout(block, ("write", 0), a_index_map)
    block = sch.reindex(main_block, ("read", 1), skip_simplify=True)
    sch.transform_layout(block, ("write", 0), b_index_map)
    block = sch.reindex(main_block, ("write", 0), skip_simplify=True)
    sch.transform_layout(block, ("read", 0), c_index_map)
    sch.transform_block_layout(main_block, matmul_index_map)
    sch.mod["main"] = sch.mod["main"].with_attr("dlight.tensorcore_prenormlized", True)
    return sch
赞(0）分享  回复(0）举报  5个月前
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