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# mypy: allow-untyped-defs
import logging
from dataclasses import dataclass
from typing import (
Any,
Callable,
Dict,
List,
Literal,
Optional,
Tuple,
TYPE_CHECKING,
Union,
)
from sympy import Expr
from torch import dtype as torch_dtype
from torch._inductor.codegen.cpp_wrapper_cpu import CppWrapperCpu
from ...autotune_process import CUDABenchmarkRequest
from ...ir import (
Buffer,
ChoiceCaller,
CUDATemplateBuffer,
IRNode,
Layout,
PrimitiveInfoType,
TensorBox,
)
from ...utils import sympy_product
from ...virtualized import V
from ..common import (
IndentedBuffer,
Kernel,
OpOverrides,
WorkspaceArg,
WorkspaceZeroMode,
)
from ..cpp_utils import CppPrinter, DTYPE_TO_CPP
if TYPE_CHECKING:
from torch._inductor.codegen.cuda.cuda_template import CUDATemplate
log = logging.getLogger(__name__)
cexpr = CppPrinter().doprint
def _normalize_idx(index: int, total_length: int) -> int:
return index if index >= 0 else index + total_length
ValidLayoutSymbols = Literal["M", "N", "K", "lda", "ldb", "ldc", "ldd"]
ValidLayoutAttrs = Literal["size", "stride"]
@dataclass(frozen=True)
class LayoutArg:
node: IRNode
symbol: ValidLayoutSymbols
attr: ValidLayoutAttrs
dim: int
def matches(self, node, attr, dim) -> bool:
return self.node == node and self.attr == attr and self.dim == dim
class CUDAKernel(Kernel):
"""
Baseclass for CUDA / Cutlass based Kernels
"""
overrides = OpOverrides # type: ignore[assignment]
def __init__(self, *args, **kwargs) -> None:
super().__init__(*args, **kwargs)
self.layout_args: Dict[str, LayoutArg] = {}
# Mapping from arg name to IRNode.
self.named_nodes: Dict[str, IRNode] = {}
def find_symbol(
self, node: IRNode, attr: ValidLayoutAttrs, dim: int
) -> Optional[str]:
arg = self.find_layout_arg(node, attr, dim)
return arg.symbol if arg else None
def find_layout_arg(
self, node: IRNode, attr: ValidLayoutAttrs, dim: int
) -> Optional[LayoutArg]:
matches = [
arg for arg in self.layout_args.values() if arg.matches(node, attr, dim)
]
assert len(matches) <= 1, matches
return None if len(matches) == 0 else matches[0]
def add_layout_arg(
self, symbol: ValidLayoutSymbols, node: IRNode, attr: ValidLayoutAttrs, dim: int
):
arg = LayoutArg(node, symbol, attr, dim)
self.layout_args.setdefault(symbol, arg)
def init_layout_args(self) -> None:
X = self.named_nodes["X"]
W = self.named_nodes["W"]
Y = self.named_nodes["Y"]
Bias = self.named_nodes.get("Bias", None)
mdim = _normalize_idx(-2, len(X.get_size()))
ndim = _normalize_idx(-1, len(W.get_size()))
kdim = _normalize_idx(-1, len(X.get_size()))
self.add_layout_arg("M", X, "size", mdim)
self.add_layout_arg("N", X, "size", ndim)
self.add_layout_arg("K", X, "size", kdim)
lda_dim = self.find_ld_idx(X)
ldb_dim = self.find_ld_idx(W)
ldc_dim = self.find_ld_idx(Y)
ldd_dim = self.find_ld_idx(Bias) if Bias else None
self.add_layout_arg("lda", X, "stride", lda_dim)
self.add_layout_arg("ldb", W, "stride", ldb_dim)
self.add_layout_arg("ldc", Y, "stride", ldc_dim)
if Bias and ldd_dim:
self.add_layout_arg("ldd", Bias, "stride", ldd_dim)
def get_layout_args(self) -> Tuple[Union[Expr, int], ...]:
X = self.named_nodes["X"]
W = self.named_nodes["W"]
Y = self.named_nodes["Y"]
Bias = self.named_nodes.get("Bias", None)
mdim = _normalize_idx(-2, len(X.get_size()))
ndim = _normalize_idx(-1, len(W.get_size()))
kdim = _normalize_idx(-1, len(X.get_size()))
def get_ld(node) -> Union[Expr, int]:
dim = self.find_ld_idx(node)
return node.get_stride()[dim]
M = X.get_size()[mdim]
N = W.get_size()[ndim]
K = X.get_size()[kdim]
LDA = get_ld(X)
LDB = get_ld(W)
LDC = get_ld(Y)
LDD = get_ld(Bias) if Bias else 0
return (M, N, K, LDA, LDB, LDC, LDD)
@staticmethod
def find_ld_idx(node: IRNode) -> int:
strides = node.get_stride()
# Handle 1D tensor case
if V.graph.sizevars.statically_known_equals(strides[-1], 1):
return _normalize_idx(-2, len(strides))
assert V.graph.sizevars.statically_known_equals(strides[-2], 1), strides[-2]
return _normalize_idx(-1, len(strides))
class CUDATemplateKernel(CUDAKernel):
"""
Template kernels defined by CUDA / Cutlass in C++.
"""
_EXTRA_CPP_ARGS = "size_t* workspace_size, uint8_t* workspace, cudaStream_t stream"
def __init__(self, kernel_name) -> None:
"""
Initializes a new instance of the CUDATemplateKernel class.
Args:
kernel_name (str): The name of the kernel.
"""
super().__init__()
self.kernel_name = kernel_name
def arg_name(self, node: IRNode) -> Optional[str]:
"""
Returns arg name of a given input or output node.
"""
if node is None:
return None
return {**self.args.input_buffers, **self.args.output_buffers}.get(
node.get_name(), None
)
def check_not_null(self, node: IRNode) -> str:
"""
Generates code to check that a node is not null.
"""
if node is None:
return ""
size_str = self.size(node, 0, -1)
name_str = self.arg_name(node)
if name_str is None:
return ""
res = IndentedBuffer(initial_indent=2)
res.tabwidth = 1
res.splice(
f"""
{{
if (!{name_str}) {{
int64_t {name_str}_size = {size_str};
if ({name_str}_size > 0) {{
throw std::runtime_error("input {name_str} is null but size is not 0!");
}}
}}
}}
"""
)
return res.getvalue()
def get_signature(self) -> str:
return self.signature
def def_kernel(
self,
inputs: List[IRNode],
outputs: List[IRNode],
names_str: str = "",
input_reorder: Optional[List[int]] = None,
) -> str:
"""
Hook called from template code to generate function definition and
needed args.
Args:
inputs: List of input IRNodes
outputs: List of output IRNodes
names_str: Comma separated list of input + output argument names.
input_reorder: The actual order of input nodes.
e.g. The template might have input argument defined as [X, W, Bias],
and the actual input passed into this template could be [Bias, X, W].
In this case, the `input_reorder` would be [2, 0, 1].
"""
names = [x.strip() for x in names_str.strip().split(",")]
if len(inputs) + len(outputs) != len(names):
raise RuntimeError(
f"{len(inputs) + len(outputs)=} != {len(names)=}, {inputs=}, {outputs=}, {names=}"
)
if input_reorder is not None:
assert len(inputs) == len(input_reorder)
else:
input_reorder = list(range(len(inputs)))
for idx in input_reorder:
name = names[idx]
node = inputs[idx]
if node is not None:
self.named_nodes[name] = node
self.args.input_buffers[node.get_name()] = name
for name, node in zip(names[len(inputs) : len(inputs) + len(outputs)], outputs):
if node is not None:
self.named_nodes[name] = node
self.args.output_buffers[node.get_name()] = name
arg_defs, *_ = self.args.cpp_argdefs()
self.init_layout_args()
size_args = [
f"const int {s}" for s in ("M", "N", "K", "lda", "ldb", "ldc", "ldd")
]
signature = f"int {self.kernel_name}({', '.join(arg_defs + size_args)}, {self._EXTRA_CPP_ARGS})"
self.signature = signature
return signature
def call_kernel(
self,
name: str,
node: "CUDATemplateBuffer", # type: ignore[name-defined]
) -> None:
"""
Generates code to call the kernel through V.graph.wrapper_code.
used from within torch._inductor.wrapper.PythonWrapperCodegen
name: Name of kernel function.
node: The CUDATemplateBuffer node which contains information about the kernel, it's fused epilogue nodes
as well as all required inputs and outputs.
"""
wrapper = V.graph.wrapper_code
if V.graph.cpp_wrapper:
# Make sure we initialize these kernels since they're exported as
# C-style symbol names.
assert isinstance(wrapper, CppWrapperCpu)
wrapper.initialized_kernels[name] = self
# We always originally initialize name with "KERNEL_NAME". So, we
# we replace with the real kernel name passed as an arg to this function.
self.signature = self.signature.replace("KERNEL_NAME", name)
_, call_args, arg_types = self.args.cpp_argdefs()
else:
_, call_args, _, arg_types = self.args.python_argdefs()
layout_args = self.get_layout_args()
call_args.extend(layout_args)
arg_types.extend("int" for a in layout_args)
# dynamo wraps unspec variable as 0d CPU tensor, need convert to scalar
for i in range(len(call_args)):
if V.graph.is_unspec_arg(call_args[i]):
call_args[i] = call_args[i] + ".item()"
elif isinstance(arg_types[i], torch_dtype):
call_args[i] = (
call_args[i]
if V.graph.cpp_wrapper
else f"c_void_p({call_args[i]}.data_ptr())"
)
# workspace_size ptr is NULL to mark this call is not intended for retrieving workspace_size.
# workspace_size should have already been retrieved prior to this call.
# workspace_size is here.
call_args.append("nullptr" if V.graph.cpp_wrapper else "None")
if V.graph.cpp_wrapper:
arg_types.append("size_t*")
if node.get_workspace_size() > 0:
ws = WorkspaceArg(
count=node.get_workspace_size(),
device=V.graph.get_current_device_or_throw(),
zero_mode=WorkspaceZeroMode.UNINITIALIZED,
outer_name=WorkspaceArg.unique_name(),
)
wrapper.generate_workspace_allocation(ws)
data_ptr = f"{ws.outer_name}.data_ptr()"
call_args.append(
data_ptr if V.graph.cpp_wrapper else f"c_void_p({data_ptr})"
)
else:
ws = None
call_args.append("nullptr" if V.graph.cpp_wrapper else "None")
if V.graph.cpp_wrapper:
arg_types.append("uint8_t*")
wrapper.generate_kernel_call(
name,
call_args,
gpu=True,
triton=False,
arg_types=arg_types,
)
if ws:
wrapper.generate_workspace_deallocation(ws)
def dtype(self, node: IRNode) -> Optional[str]:
"""
Generates code which represents dtype of a given node.
"""
if node is None:
return "void"
return DTYPE_TO_CPP.get(node.get_layout().dtype)
def cutlass_dtype(self, node: IRNode, default_dtype="void") -> Optional[str]:
# Helper method, called into from CUTLASSGemmTemplate
if node is None:
return default_dtype
from torch._inductor.codegen.cuda.cuda_template import CUTLASSTemplate
return CUTLASSTemplate._DTYPE_TO_CUTLASS[node.get_layout().dtype]
def max_valid_index(self, node: IRNode, default=-1):
# Helper method, called into from CUTLASSGemmTemplate
if node is None:
return default
max_valid_offset = 0
for i in range(len(node.get_size())):
max_valid_offset += (node.get_size()[i] - 1) * node.get_stride()[i]
return max_valid_offset
def offset(self, node: IRNode) -> str:
"""
Generates code which represents offset of a given node.
"""
if node is None:
return "0"
return str(node.get_layout().offset) # type: ignore[union-attr]
def ptr(self, node: IRNode) -> str:
"""
Generates code which represents pointer of a given node.
"""
if node is None:
return "nullptr"
arg_name = self.arg_name(node)
if arg_name is None:
return "nullptr"
offset = self.offset(node)
return arg_name if offset == "0" else f"{arg_name} + {offset}"
def size(
self,
node: IRNode,
start_index: int,
end_index: Optional[int] = None,
default_value: int = 0,
) -> str:
"""
Hook called from template code to get the size of an arg.
Generates code which represents size of a given node in [start_index, end_index).
If node is None, returns default_value.
TODO: Will add needed args to pass it in if it is dynamic.
"""
if node is None:
return str(default_value)
start_index = _normalize_idx(start_index, len(node.get_size()))
if end_index is None:
end_index = start_index
end_index = _normalize_idx(end_index, len(node.get_size()))
sizes = [
self.find_symbol(node, "size", dim=i) or node.get_size()[i]
for i in range(start_index, end_index + 1)
]
if len(sizes) == 0:
return str(default_value)
val = sympy_product(sizes)
return val
def stride(self, node: IRNode, index: int, default_value: int = 0) -> str:
"""
Hook called from template code to get the stride of an arg.
Generates code which represents stride of a given node at index.
If node is None, returns default_value.
TODO: Will add needed args to pass it in if it is dynamic.
"""
if node is None:
return str(default_value)
index = _normalize_idx(index, len(node.get_size()))
if index < 0:
return str(default_value)
stride = node.get_stride()[index]
if V.graph.sizevars.statically_known_leq(stride, 1):
return str(stride)
return self.find_symbol(node, "stride", dim=index) or str(stride)
def row_or_column_stride(self, node: IRNode, default_value: int = 0) -> str:
"""
Hook called from template code to get the row or column stride of an arg.
This is required by some CUTLASS 2.X APIs.
If the node is in row_major, it returns stride[-2].
If the node is in column_major, it returns stride[-1].
TODO: Will add needed args to pass it in if it is dynamic.
"""
if node is None or len(node.get_stride()) < 2:
return str(default_value)
stride0 = node.get_stride()[-1]
stride1 = node.get_stride()[-2]
if stride0 == 1:
return cexpr(self.rename_indexing(stride1))
elif stride1 == 1:
return cexpr(self.rename_indexing(stride0))
else:
raise RuntimeError(
f"At least 1 stride should be 1. Strides: {node.get_stride()=}"
)
class CUDATemplateCaller(ChoiceCaller):
"""
CUDATemplateCaller
This class represents a caller for CUDA template kernels. It is a subclass of ChoiceCaller.
Attributes:
name (str): The name of the caller.
category (str): The category of the caller.
bmreq (CUDABenchmarkRequest): The benchmark request for the caller.
template_buffer (CUDATemplateBuffer): The template buffer for the caller.
"""
def __init__(
self,
name: str,
category: str,
input_nodes: List[Buffer],
layout: Layout,
make_kernel_render: Callable[[CUDATemplateBuffer, Optional[List[IRNode]]], str],
bmreq: CUDABenchmarkRequest,
template: "CUDATemplate", # type: ignore[name-defined]
info_kwargs: Optional[Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]]], # type: ignore[type-arg]
description: str,
) -> None:
super().__init__(name, input_nodes, layout, description)
self.category = category
self.make_kernel_render = make_kernel_render
self.bmreq = bmreq
self.template = template
self.info_kwargs = info_kwargs
def precompile(self) -> None:
assert self.bmreq is not None
self.bmreq.precompile()
def benchmark(self, *args, out) -> float:
assert self.bmreq is not None
return self.bmreq.benchmark(
*args, output_tensor=out
) # @TODO: Hack for ensuring that Cutlass Kernel is preferred
def __str__(self) -> str:
return f"CUDATemplateCaller(source_file={self.bmreq.source_file})"
def call_name(self) -> str:
return f"cuda_template_kernels.{self.name}"
def hash_key(self) -> str:
return "-".join(
[
self.category,
self.bmreq.hash_key,
]
)
def info_dict(self) -> Dict[str, Union[PrimitiveInfoType, List[PrimitiveInfoType]]]:
"""Information returned here is logged to the autotune log file when that is enabled."""
if self.info_kwargs is not None and "op" in self.info_kwargs:
op: Any = self.info_kwargs["op"]
return {
"backend": "CUDA",
"op_type": type(op).__name__,
"op_conf_name": str(op.configuration_name()),
"op_arch": str(op.arch),
"tile_shape": str(op.tile_description.tile_shape),
"epilogue_schedule": str(op.epilogue_schedule),
"kernel_schedule": str(op.kernel_schedule),
"element_accumulator": str(op.accumulator_type()),
"op_name": str(op.procedural_name()),
"instruction_shape": str(
op.tile_description.math_instruction.instruction_shape
),
}
else:
return {"backend": "CUDA", "op_type": "unknown"}
def output_node(self) -> TensorBox:
self.bmreq.update_workspace_size()
return TensorBox.create(
CUDATemplateBuffer(
layout=self.layout,
inputs=self.input_nodes,
make_kernel_render=self.make_kernel_render,
workspace_size=self.bmreq.workspace_size,
template=self.template,
)
)
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