Russell Bryant
e489ad7a21
- **Add SPDX license headers to python source files** - **Check for SPDX headers using pre-commit** commit 9d7ef44c3cfb72ca4c32e1c677d99259d10d4745 Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:18:24 2025 -0500 Add SPDX license headers to python source files This commit adds SPDX license headers to python source files as recommended to the project by the Linux Foundation. These headers provide a concise way that is both human and machine readable for communicating license information for each source file. It helps avoid any ambiguity about the license of the code and can also be easily used by tools to help manage license compliance. The Linux Foundation runs license scans against the codebase to help ensure we are in compliance with the licenses of the code we use, including dependencies. Having these headers in place helps that tool do its job. More information can be found on the SPDX site: - https://spdx.dev/learn/handling-license-info/ Signed-off-by: Russell Bryant <rbryant@redhat.com> commit 5a1cf1cb3b80759131c73f6a9dddebccac039dea Author: Russell Bryant <rbryant@redhat.com> Date: Fri Jan 31 14:36:32 2025 -0500 Check for SPDX headers using pre-commit Signed-off-by: Russell Bryant <rbryant@redhat.com> --------- Signed-off-by: Russell Bryant <rbryant@redhat.com>
91 lines
3.0 KiB
Python
91 lines
3.0 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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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 RedundantReshapesPass(VllmInductorPass):
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"""
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This is an inductor pass that removes redundant reshape 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.
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Example graph:
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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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"""
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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_reshapes")
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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 all(
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self.dims_equivalent(s, i_s)
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for s, i_s in zip(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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logger.debug("Removed %s no-op reshapes", count)
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self.dump_graph(graph, "after_reshapes")
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self.end_and_log()
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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
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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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