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# mypy: allow-untyped-defs
from __future__ import annotations
import logging
from typing import Optional, Sequence
import torch
from torch import _prims, Tensor
log = logging.getLogger(__name__)
def make_prim(
schema: str,
impl_aten,
return_type=_prims.RETURN_TYPE.NEW,
doc: str = "",
tags: Optional[Sequence[torch.Tag]] = None,
):
if isinstance(return_type, tuple):
def meta(*args, **kwargs):
return tuple(_prims.TensorMeta(o) for o in impl_aten(*args, **kwargs))
else:
def meta(*args, **kwargs):
return _prims.TensorMeta(impl_aten(*args, **kwargs))
return _prims._make_prim(
schema=schema,
return_type=return_type,
meta=meta,
impl_aten=impl_aten,
doc=doc,
tags=tags,
)
def eager_force_stride(input_tensor: Tensor, stride) -> Tensor:
if input_tensor.stride() == stride:
return input_tensor
new_tensor = input_tensor.clone().as_strided(
input_tensor.shape,
stride,
)
new_tensor.copy_(input_tensor)
return new_tensor
# Custom prims used for handling randomness
seed = make_prim(
"inductor_seed(Device device) -> Tensor",
lambda device: torch.randint(2**63 - 1, [], device=device),
doc="create a fresh seed (one per call) for use with inductor_rand",
tags=(torch.Tag.nondeterministic_seeded,),
)
seeds = make_prim(
"inductor_seeds(int count, Device device) -> Tensor",
lambda count, device: torch.randint(2**63 - 1, [count], device=device),
doc="Horizontal fusion of many inductor_seed() calls",
tags=(torch.Tag.nondeterministic_seeded,),
)
lookup_seed = make_prim(
# if inductor_lookup_seed changes, update partitioners.py
"inductor_lookup_seed(Tensor seeds, int index) -> Tensor",
lambda seeds, index: seeds[index],
doc="Extract a single seed from the result of inductor_seeds()",
)
# inductor_random() doesn't accept a dtype.
# instead, its lowering always burns in float32, and conversions to a different type
# are explicit in the graph. We therefore need this impl (used during tracing) to hardcoded
# the dtype, so it always faithfully produces a float32 tensor during tracing,
# even if the default dtype is set to something else.
random = make_prim(
"inductor_random(SymInt[] size, Tensor seed, str mode) -> Tensor",
lambda size, seed, mode: getattr(torch, mode)(
size, device=seed.device, dtype=torch.float32
),
doc="torch.rand()/torch.randn() using backend-specific RNG that can be fused",
)
randint = make_prim(
"inductor_randint(SymInt low, SymInt high, SymInt[] size, Tensor seed) -> Tensor",
lambda low, high, size, seed: torch.randint(low, high, size, device=seed.device),
doc="torch.randint() using backend-specific RNG that can be fused",
)
force_stride_order = make_prim(
"inductor_force_stride_order(Tensor input, SymInt[] stride) -> Tensor",
eager_force_stride,
doc="Force the stride order for input tensor. No-op if the input tensor already has the stride. Do a copy otherwise",
)
_unsafe_index_put_ = make_prim(
"_unsafe_index_put_(Tensor(a!) self, Tensor?[] indices, Tensor values, bool accumulate=False) -> Tensor(a!)",
lambda self, indices, values, accumulate=False: torch.ops.aten.index_put_(
self, indices, values, accumulate
),
doc="Unsafe index_put_ (doesn't issue device asserts)",
)
fma = make_prim(
"fma(Tensor a, Tensor b, Tensor c) -> Tensor",
lambda a, b, c: (a * b) + c,
doc="Fused multiply add: fma(a, b, c) -> (a * b) + c without rounding after the multiplication",
)
def _low_memory_max_pool2d_with_offsets_aten(
self,
kernel_size,
stride,
padding,
dilation,
ceil_mode,
):
vals, indices = torch.ops.aten.max_pool2d_with_indices(
self, kernel_size, stride, padding, dilation, ceil_mode
)
input_width = self.shape[-1]
kernel_width = kernel_size[1]
bh_shape = [1] * self.ndim
bh_shape[-2] = -1
bh = torch.arange(indices.shape[-2], dtype=torch.int64, device=self.device).view(
bh_shape
)
bw_shape = [1] * self.ndim
bw_shape[-1] = -1
bw = torch.arange(indices.shape[-1], dtype=torch.int64, device=self.device).view(
bw_shape
)
hbase = bh * stride[0] - padding[0]
wbase = bw * stride[1] - padding[1]
ih = indices // input_width
iw = indices - (ih * input_width)
h_inc = ih - hbase
w_inc = iw - wbase
offsets = h_inc * kernel_width + w_inc
return vals, offsets.to(torch.int8)
def _low_memory_max_pool2d_offsets_to_indices_aten(
offsets, kernel_width, input_width, stride, padding
):
offsets = offsets.to(torch.int64)
h_inc = offsets // kernel_width
w_inc = offsets - (h_inc * kernel_width)
bh_shape = [1] * offsets.ndim
bh_shape[-2] = -1
bh = torch.arange(offsets.shape[-2], dtype=torch.int64, device=offsets.device).view(
bh_shape
)
bw_shape = [1] * offsets.ndim
bw_shape[-1] = -1
bw = torch.arange(offsets.shape[-1], dtype=torch.int64, device=offsets.device).view(
bw_shape
)
hbase = bh * stride[0] - padding[0]
wbase = bw * stride[1] - padding[1]
ih = hbase + h_inc
iw = wbase + w_inc
return ih * input_width + iw
_low_memory_max_pool2d_with_offsets = make_prim(
"_low_memory_max_pool2d_with_offsets(Tensor self, SymInt[2] kernel_size, SymInt[2] stride, SymInt[2] padding, SymInt[2] dilation, bool ceil_mode) -> (Tensor, Tensor)", # noqa: B950
_low_memory_max_pool2d_with_offsets_aten,
return_type=(_prims.RETURN_TYPE.NEW, _prims.RETURN_TYPE.NEW),
doc="Instead of returning indices, returns indices offsets.",
)
_low_memory_max_pool2d_offsets_to_indices = make_prim(
"_low_memory_max_pool2d_offsets_to_indices(Tensor self, SymInt kernel_w, SymInt input_w, SymInt[2] stride, SymInt[2] padding) -> Tensor", # noqa: B950
_low_memory_max_pool2d_offsets_to_indices_aten,
doc="Convert small int offsets to regular indices.",
)
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