[Model] Support DeepSeek-V4
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chenxb002
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vllm_mlu/compilation/fix_functionalization.py
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185
vllm_mlu/compilation/fix_functionalization.py
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# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM-MLU project
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# SPDX-License-Identifier: Apache-2.0
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import operator
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from typing import Dict, Iterable, List, Optional, Tuple, Union
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import torch
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from torch._higher_order_ops.auto_functionalize import auto_functionalized
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from vllm.compilation.vllm_inductor_pass import VllmInductorPass
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from vllm.platforms import current_platform
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from vllm.logger import init_logger
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from vllm.compilation.fix_functionalization import FixFunctionalizationPass
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from vllm.compilation.fx_utils import is_func
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from vllm_mlu.mlu_hijack_utils import MluHijackObject
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logger = init_logger(__name__)
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class FixFunctionalizationPass_MluHijack(FixFunctionalizationPass):
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@VllmInductorPass.time_and_log
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def __call__(self, graph: torch.fx.Graph):
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# XPU does not support auto-functionalization yet.
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# Will enable this when switch to vllm-xpu-kernels.
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if current_platform.is_xpu():
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logger.debug(
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"XPU platform does not support fix functionalizationpass currently."
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)
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return
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self.nodes_to_remove: list[torch.fx.Node] = []
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count = 0
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for node in graph.nodes:
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'''
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=============================
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Modify by vllm_mlu
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=============================
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@brief: skip custom op on mlu
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'''
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if current_platform.is_out_of_tree():
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continue # skip the count on mlu
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'''
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==================
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End of MLU Hijack
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==================
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'''
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if not is_func(node, auto_functionalized):
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continue # Avoid deep if-elif nesting
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kwargs = node.kwargs
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at_target = node.args[0]
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if at_target == torch.ops._C.rotary_embedding.default:
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query = kwargs["query"]
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key = kwargs["key"]
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getitem_nodes = self.getitem_users(node)
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if (
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is_func(query, operator.getitem)
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and is_func(key, operator.getitem)
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and query.args[0] == key.args[0]
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and is_func(query.args[0], torch.ops.aten.split_with_sizes.default)
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and all(
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is_func(user, torch.ops.aten.slice_scatter.default)
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for getitem_node in getitem_nodes.values()
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for user in getitem_node.users
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)
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):
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# Pattern where query and key are slices of an mm_node.
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# While functionalized, results at [1] and [2] are scattered
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# back into mm_node. So after de-functionalization, we can
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# just use mm_node directly.
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mm_node = query.args[0].args[0]
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for user in getitem_nodes.values():
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for user_of_getitem in user.users:
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if is_func(
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user_of_getitem, torch.ops.aten.slice_scatter.default
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):
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user_of_getitem.replace_all_uses_with(mm_node)
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self._remove(user_of_getitem)
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self._remove(user)
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self.insert_defunctionalized(graph, node)
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self._remove(node)
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else:
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# Directly replace the auto_functionalize(rotary_embedding)
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# with the inplace rotary_embedding. In theory, we shouldn't
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# do this blindly, but in practice in vLLM it's ok. The best
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# solution is to use auto_functionalization_v2 and then use
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# inductor's builtin defunctionalization (reinplacing) pass.
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mutated_args = {1: "query", 2: "key"}
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self.defunctionalize(graph, node, mutated_args)
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# rms_norm replacements avoid the most copies for LLaMa.
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elif at_target == torch.ops._C.fused_add_rms_norm.default:
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mutated_args = {1: "input", 2: "residual"}
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self.defunctionalize(graph, node, mutated_args)
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elif at_target == torch.ops._C.fused_add_rms_norm_static_fp8_quant.default: # noqa: E501
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mutated_args = {1: "result", 2: "residual"}
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self.defunctionalize(graph, node, mutated_args)
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elif at_target == torch.ops._C.rms_norm_dynamic_per_token_quant.default: # noqa: E501
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mutated_args = {1: "result", 2: "scale", 3: "residual"}
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self.defunctionalize(graph, node, mutated_args)
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elif at_target in [
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torch.ops._C.rms_norm.default,
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torch.ops._C.rms_norm_static_fp8_quant.default,
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]:
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mutated_args = {1: "result"}
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self.defunctionalize(graph, node, mutated_args)
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# For some reason we need to specify the args for both
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# silu_and_mul and silu_and_mul_quant. The kwargs
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# pathway gets the wrong answer.
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elif at_target == torch.ops._C.silu_and_mul.default:
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mutated_args = {1: "result"}
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self.defunctionalize(
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graph, node, mutated_args, args=("result", "input")
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)
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elif at_target == torch.ops._C.silu_and_mul_quant.default:
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mutated_args = {1: "result"}
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self.defunctionalize(
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graph, node, mutated_args, args=("result", "input", "scale")
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)
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elif (
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hasattr(torch.ops._C, "silu_and_mul_nvfp4_quant")
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and at_target == torch.ops._C.silu_and_mul_nvfp4_quant.default
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):
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mutated_args = {1: "result", 2: "result_block_scale"}
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self.defunctionalize(
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graph,
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node,
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mutated_args,
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args=(
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"result",
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"result_block_scale",
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"input",
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"input_global_scale",
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),
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)
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# Defunctionalize fused_qk_norm_rope to remove higher-order wrapper.
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elif at_target == torch.ops._C.fused_qk_norm_rope.default:
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mutated_args = {1: "qkv"}
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args = (
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"qkv",
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"num_heads_q",
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"num_heads_k",
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"num_heads_v",
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"head_dim",
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"eps",
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"q_weight",
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"k_weight",
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"cos_sin_cache",
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"is_neox",
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"position_ids",
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)
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self.defunctionalize(graph, node, mutated_args=mutated_args, args=args)
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else:
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continue # skip the count
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count += 1
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self.dump_graph(graph, "before_cleanup")
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# Remove the nodes all at once
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count_removed = len(self.nodes_to_remove)
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for node in self.nodes_to_remove:
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graph.erase_node(node)
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logger.debug(
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"De-functionalized %s nodes, removed %s nodes", count, count_removed
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)
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self.nodes_to_remove.clear()
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MluHijackObject.apply_hijack(
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FixFunctionalizationPass,
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FixFunctionalizationPass.__call__,
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FixFunctionalizationPass_MluHijack.__call__
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)
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