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137 lines
5.1 KiB
Python
137 lines
5.1 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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from collections.abc import Iterable
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from typing import Union
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import torch.fx
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from torch import SymInt
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from vllm.logger import init_logger
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from .fx_utils import is_func
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from .vllm_inductor_pass import VllmInductorPass
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logger = init_logger(__name__)
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class NoOpEliminationPass(VllmInductorPass):
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"""
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This is an inductor pass that removes redundant reshape/slice operations.
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It is required for RMSNorm-quant fusion to work properly.
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That's because apply_fp8_linear adds a reshape, which is redundant
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in the 2D-case. Additionally, torch internal no-op elimination pass does
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not handle certain slice variants.
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Example graph 1:
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getitem_1: "f16[s0, 4096]" = ...
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view_1: "f16[s0, 4096]" = torch.reshape(getitem_1, [-1, 4096])
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at = auto_functionalized(static_scaled_fp8_quant, input = view_1, ...)
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out: "f8e4m3fn[s0, 4096]" = at[1]
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Can be replaced with:
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getitem_1: "f16[s0, 4096]" = ...
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at = auto_functionalized(static_scaled_fp8_quant, input = getitem_1, ...)
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out: "f8e4m3fn[s0, 4096]" = at[1]
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Example graph 2:
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arg0: "s0" = SymInt(s0)
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scaled_mm: "f16[s0, 4096]" = ...
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slice_1: "f16[s0, 4096]" = torch.slice(scaled_mm, -1, 0, arg0)
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at = auto_functionalized(fused_add_rms_norm, input = slice_1, ...)
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out: "f16[s0, 4096]" = torch.slice_scatter(scaled_mm, at[1], 0, 0, arg0)
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Can be replaced with:
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arg0: "s0" = SymInt(s0)
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scaled_mm: "f16[s0, 4096]" = ...
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at = auto_functionalized(fused_add_rms_norm, input = scaled_mm, ...)
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out: "f16[s0, 4096]" = at[1]
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TODO(luka): This is currently tested in test_fusion,
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but separate tests could be good.
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"""
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def __call__(self, graph: torch.fx.Graph):
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self.begin()
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self.dump_graph(graph, "before_noop_elimination")
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count = 0
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# Remove no-op reshapes/views:
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for node in graph.nodes:
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if is_func(node, torch.ops.aten.reshape.default):
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input, shape = node.args[:2]
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input_shape = input.meta["val"].shape
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if len(shape) != len(input_shape):
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# Reshape changing rank, skip
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continue
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if shape.count(-1) > 1:
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# Invalid reshape args, skip
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continue
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if self.all_dims_equivalent(shape, input_shape):
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node.replace_all_uses_with(input)
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graph.erase_node(node)
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count += 1
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elif is_func(node, torch.ops.aten.slice.Tensor):
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input, dim_index, start, end = node.args[:4]
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input_shape = input.meta["val"].shape
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i_dim = input_shape[dim_index]
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if start == 0 and self.dims_equivalent(end, i_dim):
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node.replace_all_uses_with(input)
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graph.erase_node(node)
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count += 1
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elif is_func(node, torch.ops.aten.slice_scatter.default):
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base, view, dim_index, start, end = node.args[:5]
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base_shape = base.meta["val"].shape
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view_shape = view.meta["val"].shape
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view_dim = view_shape[dim_index]
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# Check that view fully covers base and the full view is used
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# (if the view fully covered the base after slicing but was not
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# fully used, we could replace slice_scatter with a simple slice
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# but that's a niche case).
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if (base_shape == view_shape and start == 0
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and self.dims_equivalent(end, view_dim)):
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node.replace_all_uses_with(view)
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graph.erase_node(node)
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count += 1
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logger.debug("Removed %s no-op reshapes and slices", count)
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self.dump_graph(graph, "after_noop_elimination")
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self.end_and_log()
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def all_dims_equivalent(self, dims: Iterable[Union[int, torch.fx.Node]],
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i_dims: Iterable[Union[int, SymInt]]):
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return all(
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self.dims_equivalent(s, i_s) for s, i_s in zip(dims, i_dims))
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def dims_equivalent(self, dim: Union[int, torch.fx.Node],
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i_dim: Union[int, SymInt]) -> bool:
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"""
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This function checks if two dimensions are equivalent.
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:param dim: The dimension arg to reshape/slice
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:param i_dim: The corresponding dimension in the input tensor
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:return: Are the dimensions equivalent?
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There are three cases in which the dimensions are equivalent:
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1. The dimensions are equal (both integers)
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2. The reshape dimension is -1 (i.e. inferred)
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3. The dimensions both correspond to the same SymInt
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While case 2 does not guarantee the dimensions are equal,
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they are equal if all other dimensions are equal.
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In case 3, the reshape dimension is a torch.fx.Node,
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and its value is a SymInt. That value is equal to the
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input dimension.
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"""
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# Case 1 and 2
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if dim == i_dim or dim == -1:
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return True
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# Case 3
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return isinstance(dim, torch.fx.Node) and dim.meta["val"] == i_dim
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