679 lines
21 KiB
Python
679 lines
21 KiB
Python
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import functools
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import logging
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import math
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import sys
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import typing
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from typing import Optional
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import torch
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import torch._decomp as decomp
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import torch._prims_common as utils
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import torch.ao.quantization.fx._decomposed
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from torch._decomp import (
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core_aten_decompositions,
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get_decompositions,
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remove_decompositions,
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)
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from torch._decomp.decompositions import (
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_grid_sampler_2d as decomp_grid_sampler_2d,
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pw_cast_for_opmath,
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)
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from torch._decomp.decompositions_for_rng import extra_random_decomps
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from torch._higher_order_ops.out_dtype import out_dtype
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from torch._prims_common import (
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elementwise_dtypes,
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ELEMENTWISE_TYPE_PROMOTION_KIND,
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type_to_dtype,
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)
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from . import config, inductor_prims
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log = logging.getLogger(__name__)
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aten = torch.ops.aten
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prims = torch.ops.prims
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quantized_decomposed = torch.ops.quantized_decomposed
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inductor_decompositions = get_decompositions(
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[
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aten._adaptive_avg_pool2d_backward,
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aten.arange,
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aten.bitwise_and_,
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aten.bitwise_or_,
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aten.clamp_min_,
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aten.dist,
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aten.empty_like,
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aten.flip,
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aten.gelu,
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aten.hardtanh,
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aten.index_select,
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aten.lcm,
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aten.leaky_relu,
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aten.linalg_vector_norm,
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aten._log_softmax,
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aten.max_pool2d_with_indices_backward,
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aten._native_batch_norm_legit,
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aten._native_batch_norm_legit_functional,
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aten._native_batch_norm_legit_no_training,
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aten.native_batch_norm,
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aten.native_group_norm,
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aten.native_layer_norm,
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aten.nll_loss2d_backward,
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aten._softmax,
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aten.sin_,
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aten.sqrt_,
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out_dtype,
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aten._to_copy,
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aten.tril_indices,
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aten.triu_indices,
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aten.upsample_bilinear2d.vec,
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]
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)
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decompositions = {**core_aten_decompositions(), **inductor_decompositions}
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# Remove unwanted decompositions included via the core ATen decompositions from
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# the Inductor decomp table.
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decomps_to_exclude = [
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aten._unsafe_index,
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aten._scaled_dot_product_flash_attention_for_cpu.default, # See comments in torch/_decomp/decompositions.py
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aten.clamp_max,
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aten.clamp_min,
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aten.glu, # inductor lowers this directly
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aten.split.Tensor, # inductor lowers this directly
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aten.squeeze, # inductor lowers this directly
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aten.sum, # inductor lowers this directly
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aten.unbind, # inductor lowers this directly
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]
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remove_decompositions(decompositions, decomps_to_exclude)
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def register_decomposition(ops):
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for op in [ops] if callable(ops) else ops:
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if op in decompositions:
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log.warning("duplicate decomp: %s", ops)
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return decomp.register_decomposition(ops, decompositions)
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# TODO: for now, inductor doesn't handle asserts
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# because the condition is symbool -> tensor in the graph.
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@register_decomposition([aten._assert_async.msg])
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def assert_async_msg_decomp(tensor, msg):
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return
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# Following `assert_async_msg_decomp` and implement as non-op.
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@register_decomposition([aten._functional_assert_async.msg])
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def functional_assert_async_msg_decomp(tensor, msg):
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return
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@register_decomposition([aten.sym_constrain_range_for_size.default])
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def sym_constrain_range_for_size(symbol, *, min=None, max=None):
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return
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@register_decomposition([aten.clamp])
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@pw_cast_for_opmath
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def clamp(x, min=None, max=None):
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if min is not None:
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x = x.clamp_min(min)
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if max is not None:
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x = x.clamp_max(max)
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return x
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@register_decomposition([aten.full])
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def full(size, fill_value, **kwargs):
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dtype = kwargs.get("dtype")
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if dtype is None:
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kwargs["dtype"] = type_to_dtype(type(fill_value))
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return aten.full(size, fill_value, **kwargs)
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return NotImplemented
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# Not really sure how to put this into the main library. PrimTorch wants
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# empty_permuted to go to the prim, and typically users don't really want
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# to decompose to empty_strided (but inductor is OK with it, because we are
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# cool with strides and everything goes to empty_strided)
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@register_decomposition([aten.empty_permuted.default])
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def empty_permuted(size, physical_layout, **kwargs):
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perm = [0] * len(size)
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for p, l in enumerate(physical_layout):
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perm[l] = p
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return torch.empty([size[l] for l in physical_layout], **kwargs).permute(perm)
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@register_decomposition([aten.convolution_backward])
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def convolution_backward(
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grad_output,
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input,
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weight,
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bias_sizes,
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stride,
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padding,
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dilation,
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transposed,
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output_padding,
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groups,
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output_mask,
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):
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if not output_mask[2] or grad_output.device.type != "cuda":
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return NotImplemented
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grad_bias = aten.sum(grad_output, [0] + list(range(2, grad_output.dim())))
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grad_inp, grad_weight, _ = aten.convolution_backward(
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grad_output,
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input,
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weight,
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bias_sizes,
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stride,
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padding,
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dilation,
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transposed,
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output_padding,
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groups,
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[output_mask[0], output_mask[1], False],
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)
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return (grad_inp, grad_weight, grad_bias)
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@register_decomposition([aten.log2])
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def log2(x):
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return torch.log(x) * (1.0 / math.log(2.0))
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@register_decomposition([aten.round.decimals])
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def round_dec(x, decimals=0):
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ten_pow_decimals = 10.0**decimals
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return aten.round(x * ten_pow_decimals) * (1.0 / ten_pow_decimals)
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@register_decomposition([aten.bmm])
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@pw_cast_for_opmath
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def bmm(self, batch2):
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if config.coordinate_descent_tuning:
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if self.shape[1] == 1 or batch2.shape[2] == 1:
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out = (self.unsqueeze(-1) * batch2.unsqueeze(1)).sum(dim=2)
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return out
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if self.device.type == "cpu":
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if self.size(1) == 1 and batch2.size(-1) == 1:
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return torch.sum(
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self.squeeze(1) * batch2.squeeze(-1), dim=1, keepdim=True
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).unsqueeze(1)
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return NotImplemented
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@register_decomposition([aten.addmm])
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@pw_cast_for_opmath
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def addmm(self, mat1, mat2, beta=1, alpha=1):
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if self.device.type == "cpu":
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if mat1.size(0) == 1 and mat2.size(-1) == 1:
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out = torch.sum(
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mat1.squeeze(0) * mat2.squeeze(-1), dim=0, keepdim=True
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).unsqueeze(0)
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return alpha * out + beta * self
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if mat1.size(0) == 1 and mat2.size(0) <= 16 and mat2.size(1) <= 16:
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out = (mat1.T * mat2).sum(dim=0, keepdim=True)
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return alpha * out + beta * self
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return NotImplemented
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@register_decomposition([aten.mm])
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@pw_cast_for_opmath
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def mm(self, input2):
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from torch.fx.experimental.symbolic_shapes import (
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definitely_true,
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guard_size_oblivious,
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)
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# Our matrix vector multiplies only achieve peak bandwidth with coordinate descent tuning.
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# todo: Look into why and fix it (hopefully)
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if config.coordinate_descent_tuning:
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if self.shape[0] == 1 or input2.shape[1] == 1:
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return (self.unsqueeze(2) * input2.unsqueeze(0)).sum(dim=1)
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if self.device.type == "cpu":
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if (
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guard_size_oblivious(self.size(-1) == 1)
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and guard_size_oblivious(self.size(0) > 0)
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and guard_size_oblivious(input2.size(0) == 1)
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and (self.dtype == input2.dtype)
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and definitely_true((torch.numel(self) + torch.numel(input2)) <= 32)
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):
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return torch.cat([self[i, :] * input2 for i in range(self.size(0))])
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if guard_size_oblivious(self.size(0) == 1) and guard_size_oblivious(
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input2.size(-1) == 1
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):
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return torch.sum(
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self.squeeze(0) * input2.squeeze(-1), dim=0, keepdim=True
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).unsqueeze(0)
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return NotImplemented
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# This pass does two things:
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# - Eliminate cat when there is only one tensor input
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# - Normalize cat calls, so that legacy empty 1-D tensors are removed (NB: we
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# don't remove ALL empty tensors, only the naughty ones)
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@register_decomposition([aten.cat.default])
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def cat(tensors, dim=0):
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from torch.fx.experimental.symbolic_shapes import guard_size_oblivious
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def non_empty_tensor(x):
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# For better or worse, this is a valid cat:
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#
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# torch.cat([torch.randn(2, 2, 4), torch.randn(0), torch.randn(3, 2, 4)])
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#
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# We'd like to eliminate naughtiness like this for downstream passes
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# like split_cat. The easiest way is to just drop such inputs
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# (guarding that they are non-zero).
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#
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# Is it permissible for this filtering to be size-oblivious? A case
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# where this could matter is cat([(2, 2), (u0,)], dim=0); if u0
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# happened to be zero, we would have liked to have filtered it out.
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# But actually, the ONLY way this could have passed is if u0 == 0,
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# so by the time we get here we have already installed a deferred
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# runtime assert forcing u0 to be zero. So if this hasn't happened,
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# we know that the unbacked SymInt has appropriate size and there are
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# no problems.
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return len(x.shape) != 1 or guard_size_oblivious(x.shape[0] > 0)
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filtered_tensors = list(filter(non_empty_tensor, tensors))
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if len(filtered_tensors) == 1:
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return filtered_tensors[0].clone()
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elif 1 < len(filtered_tensors) < len(tensors):
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# on the first call, when we remove empty tensors, we redispatch recursively
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return aten.cat.default(filtered_tensors, dim)
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# when no 'filtering' has occurred, we raise to prevent infinite recursion (no more decomposition needed)
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return NotImplemented
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@register_decomposition([aten.angle])
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def angle(x):
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if x.is_complex():
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return torch.where(
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torch.isnan(x.real), float("nan"), torch.atan2(x.imag, x.real)
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)
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# when x is real number
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# if x >= 0, return 0
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# if x < 0, return pi
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# if x is nan, return nan
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_, dtype = elementwise_dtypes(
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x,
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type_promotion_kind=ELEMENTWISE_TYPE_PROMOTION_KIND.INT_TO_FLOAT,
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)
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pi = torch.scalar_tensor(math.pi, dtype=dtype, device=x.device)
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ret = torch.where(x < 0, pi, 0.0)
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return torch.where(torch.isnan(x), float("nan"), ret)
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@register_decomposition([aten.add])
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def add(x, y, *, alpha=None):
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x_is_complex_tensor = torch.is_tensor(x) and x.is_complex()
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y_is_complex_tensor = torch.is_tensor(y) and y.is_complex()
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if not x_is_complex_tensor or not y_is_complex_tensor:
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return NotImplemented
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z = y
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if alpha is not None:
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z = alpha * y
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complex_type = torch.promote_types(x.dtype, y.dtype)
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return (x.view(x.real.dtype) + z.view(y.real.dtype)).view(complex_type)
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@register_decomposition([aten.conj_physical])
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def conj_physical(self):
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assert not self.is_complex(), "TODO: implement this"
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return self
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@register_decomposition([aten.lift, aten.detach_])
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def lift(self):
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return self
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@register_decomposition([aten.bernoulli.default])
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def bernoulli(self, *, generator=None):
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assert generator is None
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return (torch.rand_like(self, dtype=torch.float32) < self).to(self.dtype)
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@register_decomposition([aten.fmin, prims.fmin])
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def fmin(self, other):
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return torch.where(torch.isnan(other) | (other > self), self, other)
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@register_decomposition([aten.fmax, prims.fmax])
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def fmax(self, other):
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return torch.where(torch.isnan(other) | (other < self), self, other)
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@register_decomposition(aten.amax)
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def amax(self, dim=None, keepdim=False):
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if self.dtype == torch.bool:
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return torch.any(self, dim=dim, keepdim=keepdim)
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return NotImplemented
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@register_decomposition(aten.amin)
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def amin(self, dim=None, keepdim=False):
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if self.dtype == torch.bool:
|
||
|
|
return torch.all(self, dim=dim, keepdim=keepdim)
|
||
|
|
return NotImplemented
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition([aten.narrow_copy])
|
||
|
|
def narrow_copy(self, dim, start, length):
|
||
|
|
return torch.narrow(self, dim, start, length).clone()
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition([aten.expand_copy])
|
||
|
|
def expand_copy(self, size, *, implicit=False):
|
||
|
|
return aten.expand(self, size, implicit=implicit).clone()
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition([aten.view_copy.default])
|
||
|
|
def view_copy_default(self, size):
|
||
|
|
return aten.view(self, size).clone()
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition([aten.view_copy.dtype])
|
||
|
|
def view_copy_dtype(self, dtype):
|
||
|
|
return self.to(dtype).clone()
|
||
|
|
|
||
|
|
|
||
|
|
def get_like_layout(
|
||
|
|
tensor: torch.Tensor, memory_format: Optional[torch.memory_format]
|
||
|
|
) -> torch.memory_format:
|
||
|
|
# TODO: _to_copy tensor to stride permutation
|
||
|
|
if memory_format is torch.preserve_format or memory_format is None:
|
||
|
|
return utils.suggest_memory_format(tensor)
|
||
|
|
else:
|
||
|
|
return memory_format
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.rand_like)
|
||
|
|
def rand_like(self, *, dtype=None, device=None, memory_format=None, **kwargs):
|
||
|
|
return torch.rand(
|
||
|
|
[*self.size()],
|
||
|
|
dtype=dtype or self.dtype,
|
||
|
|
device=device or self.device,
|
||
|
|
**kwargs,
|
||
|
|
).to(memory_format=get_like_layout(self, memory_format))
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.randn_like)
|
||
|
|
def randn_like(self, *, dtype=None, device=None, memory_format=None, **kwargs):
|
||
|
|
return torch.randn(
|
||
|
|
[*self.size()],
|
||
|
|
dtype=dtype or self.dtype,
|
||
|
|
device=device or self.device,
|
||
|
|
**kwargs,
|
||
|
|
).to(memory_format=get_like_layout(self, memory_format))
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.full_like)
|
||
|
|
def full_like(
|
||
|
|
self,
|
||
|
|
fill_value,
|
||
|
|
*,
|
||
|
|
dtype=None,
|
||
|
|
layout=None,
|
||
|
|
device=None,
|
||
|
|
pin_memory=False,
|
||
|
|
requires_grad=False,
|
||
|
|
memory_format=torch.preserve_format,
|
||
|
|
):
|
||
|
|
return torch.full(
|
||
|
|
[*self.size()],
|
||
|
|
fill_value,
|
||
|
|
dtype=dtype or self.dtype,
|
||
|
|
layout=layout or self.layout,
|
||
|
|
device=device or self.device,
|
||
|
|
requires_grad=requires_grad,
|
||
|
|
).to(memory_format=get_like_layout(self, memory_format))
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.randint_like.default)
|
||
|
|
def randint_like(self, high, *, dtype=None, device=None, memory_format=None, **kwargs):
|
||
|
|
return aten.randint.low(
|
||
|
|
0,
|
||
|
|
high,
|
||
|
|
[*self.size()],
|
||
|
|
dtype=dtype or self.dtype,
|
||
|
|
device=device or self.device,
|
||
|
|
**kwargs,
|
||
|
|
).to(memory_format=get_like_layout(self, memory_format))
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.randint_like.low_dtype)
|
||
|
|
def randint_like_low(
|
||
|
|
self, low, high, *, dtype=None, device=None, memory_format=None, **kwargs
|
||
|
|
):
|
||
|
|
return aten.randint.low(
|
||
|
|
low,
|
||
|
|
high,
|
||
|
|
[*self.size()],
|
||
|
|
dtype=dtype or self.dtype,
|
||
|
|
device=device or self.device,
|
||
|
|
**kwargs,
|
||
|
|
).to(memory_format=get_like_layout(self, memory_format))
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.randint.default)
|
||
|
|
def randint(high, size, **kwargs):
|
||
|
|
return aten.randint.low(0, high, size, **kwargs)
|
||
|
|
|
||
|
|
|
||
|
|
# The difference between quantize_per_tensor.default and quantize_per_tensor.tensor is
|
||
|
|
# scale and zero_point is scalar or scalar tensor
|
||
|
|
@register_decomposition(quantized_decomposed.quantize_per_tensor.default)
|
||
|
|
def quantize_per_tensor_default_decomp_impl(
|
||
|
|
input: torch.Tensor,
|
||
|
|
scale: float,
|
||
|
|
zero_point: int,
|
||
|
|
quant_min: int,
|
||
|
|
quant_max: int,
|
||
|
|
dtype: torch.dtype,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
if input.dtype == torch.bfloat16:
|
||
|
|
input = input.to(torch.float32)
|
||
|
|
inv_scale = 1.0 / scale
|
||
|
|
return torch.clamp(
|
||
|
|
torch.round(input * inv_scale) + zero_point, quant_min, quant_max
|
||
|
|
).to(dtype)
|
||
|
|
|
||
|
|
|
||
|
|
# The difference between dequantize_per_tensor.default and dequantize_per_tensor.tensor is
|
||
|
|
# scale and zero_point is scalar or scalar tensor
|
||
|
|
@register_decomposition(quantized_decomposed.dequantize_per_tensor.default)
|
||
|
|
def dequantize_per_tensor_default_decomp_impl(
|
||
|
|
input: torch.Tensor,
|
||
|
|
scale: float,
|
||
|
|
zero_point: int,
|
||
|
|
quant_min: int,
|
||
|
|
quant_max: int,
|
||
|
|
dtype: torch.dtype,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
return (input.to(torch.float32) - zero_point) * scale
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(quantized_decomposed.quantize_per_tensor.tensor)
|
||
|
|
def quantize_per_tensor_tensor_decomp_impl(
|
||
|
|
input: torch.Tensor,
|
||
|
|
scale: torch.Tensor,
|
||
|
|
zero_point: torch.Tensor,
|
||
|
|
quant_min: int,
|
||
|
|
quant_max: int,
|
||
|
|
dtype: torch.dtype,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
if input.dtype == torch.bfloat16:
|
||
|
|
input = input.to(torch.float32)
|
||
|
|
inv_scale = 1.0 / scale
|
||
|
|
return torch.clamp(
|
||
|
|
torch.round(input * inv_scale) + zero_point, quant_min, quant_max
|
||
|
|
).to(dtype)
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(quantized_decomposed.dequantize_per_tensor.tensor)
|
||
|
|
def dequantize_per_tensor_tensor_decomp_impl(
|
||
|
|
input: torch.Tensor,
|
||
|
|
scale: torch.Tensor,
|
||
|
|
zero_point: torch.Tensor,
|
||
|
|
quant_min: int,
|
||
|
|
quant_max: int,
|
||
|
|
dtype: torch.dtype,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
return (input.to(torch.float32) - zero_point.to(torch.int32)) * scale.to(
|
||
|
|
torch.float32
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(torch.ops.quantized.embedding_bag_byte_unpack)
|
||
|
|
def q_embedding_bag_byte_unpack_decomp(packed):
|
||
|
|
def bitcast_u8_to_f32(u8):
|
||
|
|
x, y, z, w = (u8[..., n].to(torch.int32) for n in (0, 1, 2, 3))
|
||
|
|
if sys.byteorder == "little":
|
||
|
|
return (x + (y << 8) + (z << 16) + (w << 24)).view(torch.float32)[..., None]
|
||
|
|
else:
|
||
|
|
return ((x << 24) + (y << 16) + (z << 8) + w).view(torch.float32)[..., None]
|
||
|
|
|
||
|
|
scales = bitcast_u8_to_f32(packed[..., -8:-4])
|
||
|
|
offsets = bitcast_u8_to_f32(packed[..., -4:])
|
||
|
|
return packed[..., :-8].to(torch.float32) * scales + offsets
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition([aten.grid_sampler_2d])
|
||
|
|
@pw_cast_for_opmath
|
||
|
|
def grid_sampler_2d(
|
||
|
|
a: torch.Tensor,
|
||
|
|
grid: torch.Tensor,
|
||
|
|
interpolation_mode: int = 0,
|
||
|
|
padding_mode: int = 0,
|
||
|
|
align_corners: bool = False,
|
||
|
|
) -> torch.Tensor:
|
||
|
|
# We do not expand the grid (_expand_grid=False) on cpu for performance reasons
|
||
|
|
# Experimenting locally it was found that compiled CUDA code is accelerated by ~5x
|
||
|
|
# and CPU code by ~2x on bicubic mode, if we expand the grid from (N, H, W, 2) into (N, C, H, W, 2)
|
||
|
|
# However, this leads to a slowdown around ~0.8x on CPU bilinear mode, channels first.
|
||
|
|
# Thus we apply this hack to not expand the grid for this case.
|
||
|
|
_expand_grid = not (
|
||
|
|
a.device == torch.device("cpu")
|
||
|
|
and interpolation_mode == 0
|
||
|
|
and a.is_contiguous(memory_format=torch.contiguous_format)
|
||
|
|
)
|
||
|
|
|
||
|
|
output = decomp_grid_sampler_2d(
|
||
|
|
a,
|
||
|
|
grid=grid,
|
||
|
|
interpolation_mode=interpolation_mode,
|
||
|
|
padding_mode=padding_mode,
|
||
|
|
align_corners=align_corners,
|
||
|
|
_expand_grid=_expand_grid,
|
||
|
|
)
|
||
|
|
return output
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten._foreach_addcmul.Scalar)
|
||
|
|
def _foreach_addcmul_scalar(self, left_tensors, right_tensors, scalar=1):
|
||
|
|
return aten._foreach_add.List(
|
||
|
|
self, aten._foreach_mul.List(left_tensors, right_tensors), alpha=scalar
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten._foreach_addcdiv.Scalar)
|
||
|
|
def _foreach_addcdiv_scalar(self, left_tensors, right_tensors, scalar=1):
|
||
|
|
return aten._foreach_add.List(
|
||
|
|
self, aten._foreach_div.List(left_tensors, right_tensors), alpha=scalar
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten._foreach_lerp.Scalar)
|
||
|
|
def _foreach_lerp_scalar(start_tensors, end_tensors, weight):
|
||
|
|
return aten._foreach_add.List(
|
||
|
|
start_tensors,
|
||
|
|
aten._foreach_mul.Scalar(
|
||
|
|
aten._foreach_sub.List(end_tensors, start_tensors), weight
|
||
|
|
),
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@aten.miopen_batch_norm.default.py_impl(torch._C.DispatchKey.Autograd)
|
||
|
|
@register_decomposition(aten.miopen_batch_norm)
|
||
|
|
def miopen_batch_norm(
|
||
|
|
input: torch.Tensor,
|
||
|
|
weight: torch.Tensor,
|
||
|
|
bias: typing.Optional[torch.Tensor],
|
||
|
|
running_mean: typing.Optional[torch.Tensor],
|
||
|
|
running_var: typing.Optional[torch.Tensor],
|
||
|
|
training: bool,
|
||
|
|
exponential_average_factor: float,
|
||
|
|
epsilon: float,
|
||
|
|
):
|
||
|
|
a, b, c = aten.native_batch_norm(
|
||
|
|
input,
|
||
|
|
weight,
|
||
|
|
bias,
|
||
|
|
running_mean,
|
||
|
|
running_var,
|
||
|
|
training,
|
||
|
|
exponential_average_factor,
|
||
|
|
epsilon,
|
||
|
|
)
|
||
|
|
|
||
|
|
if training:
|
||
|
|
return (a, b, c)
|
||
|
|
return (
|
||
|
|
a,
|
||
|
|
weight.new_zeros((0,)),
|
||
|
|
weight.new_zeros((0,)),
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
@functools.lru_cache(None)
|
||
|
|
def fast_random_decomps():
|
||
|
|
return {**decompositions, **extra_random_decomps}
|
||
|
|
|
||
|
|
|
||
|
|
def select_decomp_table():
|
||
|
|
"""decomps can change based on config"""
|
||
|
|
if config.fallback_random:
|
||
|
|
return decompositions
|
||
|
|
return fast_random_decomps()
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.masked_scatter)
|
||
|
|
def masked_scatter(self, mask, source):
|
||
|
|
if self.device.type == "cuda":
|
||
|
|
# This two-step algorithm is the same as eager CUDA, for eager CPU we
|
||
|
|
# use a 1-shot serial iteration.
|
||
|
|
self, mask = aten.broadcast_tensors([self, mask])
|
||
|
|
source_idx = mask.reshape(-1).cumsum(0) - 1
|
||
|
|
return inductor_prims.masked_scatter_with_index(self, mask, source_idx, source)
|
||
|
|
return NotImplemented
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(quantized_decomposed.choose_qparams.tensor)
|
||
|
|
def choose_qparams_tensor(
|
||
|
|
input: torch.Tensor, quant_min: int, quant_max: int, eps: float, dtype: torch.dtype
|
||
|
|
):
|
||
|
|
min_val, max_val = torch.aminmax(input)
|
||
|
|
scale = (max_val - min_val) / float(quant_max - quant_min)
|
||
|
|
scale = torch.max(scale, torch.Tensor([eps]))
|
||
|
|
zero_point = quant_min - torch.round(min_val / scale).to(torch.int)
|
||
|
|
zero_point = torch.clamp(zero_point, quant_min, quant_max)
|
||
|
|
return scale.to(torch.float64), zero_point.to(torch.int64)
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.put)
|
||
|
|
def put(self, index, source, accumulate=False):
|
||
|
|
flattened = self.flatten()
|
||
|
|
flattened = torch.index_put(
|
||
|
|
flattened, [index], source.reshape(index.shape), accumulate
|
||
|
|
)
|
||
|
|
return flattened.reshape(self.shape)
|
||
|
|
|
||
|
|
|
||
|
|
@register_decomposition(aten.put_)
|
||
|
|
def put_(self, index, source, accumulate=False):
|
||
|
|
out = aten.put(self, index, source, accumulate=accumulate)
|
||
|
|
return self.copy_(out)
|