244
vllm_ascend/compilation/passes/qknorm_rope_fusion_pass.py
Normal file
244
vllm_ascend/compilation/passes/qknorm_rope_fusion_pass.py
Normal file
@@ -0,0 +1,244 @@
|
||||
#
|
||||
# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
|
||||
# This file is a part of the vllm-ascend project.
|
||||
#
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
#
|
||||
import torch
|
||||
from torch._inductor.pattern_matcher import PatternMatcherPass, PatternPrettyPrinter
|
||||
from vllm.compilation.passes.vllm_inductor_pass import VllmInductorPass
|
||||
from vllm.config import VllmConfig, get_layers_from_vllm_config
|
||||
from vllm.config.compilation import Range
|
||||
from vllm.logger import logger
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||||
from vllm.model_executor.layers.attention import Attention
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||||
|
||||
from vllm_ascend.compilation.passes.base_pattern import BasePattern
|
||||
from vllm_ascend.device.device_op import DeviceOperator
|
||||
from vllm_ascend.utils import get_rope_dim
|
||||
|
||||
|
||||
class QKNormRopeFusionPattern(BasePattern):
|
||||
def __init__(self, vllm_config, head_dim, num_heads, num_kv_heads, eps=1e-6):
|
||||
super().__init__(vllm_config, eps)
|
||||
self.head_dim = head_dim
|
||||
self.num_heads = num_heads
|
||||
self.num_kv_heads = num_kv_heads
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.device = vllm_config.device_config.device if vllm_config.device_config else None
|
||||
self.rope_dim = get_rope_dim(vllm_config)
|
||||
|
||||
def get_inputs(self):
|
||||
T = 5
|
||||
max_position_embeddings = 16384
|
||||
qkv = torch.empty(T, self.q_size + 2 * self.kv_size, dtype=torch.bfloat16, device="npu")
|
||||
q_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
k_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
cos_sin_cache = torch.empty(max_position_embeddings, self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
positions = torch.ones(T, dtype=torch.int64, device="npu")
|
||||
return [qkv, q_weight, k_weight, cos_sin_cache, positions]
|
||||
|
||||
def get_pattern(self):
|
||||
def pattern(
|
||||
qkv: torch.Tensor,
|
||||
q_weight: torch.Tensor,
|
||||
k_weight: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
):
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
|
||||
q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
|
||||
q_norm_out, _ = torch.ops.npu.npu_rms_norm(q_by_head, q_weight, self.eps)
|
||||
|
||||
k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
|
||||
k_norm_out, _ = torch.ops.npu.npu_rms_norm(k_by_head, k_weight, self.eps)
|
||||
|
||||
q_flat = q_norm_out.view(q.shape)
|
||||
k_flat = k_norm_out.view(k.shape)
|
||||
q_rope, k_rope = torch.ops.vllm.npu_rotary_embedding(
|
||||
positions, q_flat, k_flat, cos_sin_cache, self.head_dim, self.rope_dim, True
|
||||
)
|
||||
|
||||
return q_rope, k_rope, v
|
||||
|
||||
return pattern
|
||||
|
||||
def get_replacement(self):
|
||||
def replacement(
|
||||
qkv: torch.Tensor,
|
||||
q_weight: torch.Tensor,
|
||||
k_weight: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
):
|
||||
results = DeviceOperator.split_qkv_rmsnorm_rope(
|
||||
input=qkv,
|
||||
q_weight=q_weight,
|
||||
k_weight=k_weight,
|
||||
q_hidden_size=self.q_size,
|
||||
kv_hidden_size=self.kv_size,
|
||||
head_dim=self.head_dim,
|
||||
eps=self.eps,
|
||||
q_bias=None,
|
||||
k_bias=None,
|
||||
cos_sin_cache=cos_sin_cache,
|
||||
positions=positions,
|
||||
)
|
||||
|
||||
return results
|
||||
|
||||
return replacement
|
||||
|
||||
|
||||
class QKNormRopeFusionPatternWithBias(BasePattern):
|
||||
def __init__(self, vllm_config, head_dim, num_heads, num_kv_heads, eps=1e-6):
|
||||
super().__init__(vllm_config, eps)
|
||||
self.head_dim = head_dim
|
||||
self.num_heads = num_heads
|
||||
self.num_kv_heads = num_kv_heads
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.device = vllm_config.device_config.device if vllm_config.device_config else None
|
||||
self.rope_dim = get_rope_dim(vllm_config)
|
||||
|
||||
def get_inputs(self):
|
||||
T = 5
|
||||
max_position_embeddings = 16384
|
||||
qkv = torch.empty(T, self.q_size + 2 * self.kv_size, dtype=torch.bfloat16, device="npu")
|
||||
q_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
k_weight = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
q_bias = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
k_bias = torch.empty(self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
cos_sin_cache = torch.empty(max_position_embeddings, self.head_dim, dtype=torch.bfloat16, device="npu")
|
||||
positions = torch.ones(T, dtype=torch.int64, device="npu")
|
||||
|
||||
return [qkv, q_weight, k_weight, q_bias, k_bias, cos_sin_cache, positions]
|
||||
|
||||
def get_pattern(self):
|
||||
def pattern(
|
||||
qkv: torch.Tensor,
|
||||
q_weight: torch.Tensor,
|
||||
k_weight: torch.Tensor,
|
||||
q_bias: torch.Tensor,
|
||||
k_bias: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
):
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
|
||||
q_by_head = q.view(*q.shape[:-1], q.shape[-1] // self.head_dim, self.head_dim)
|
||||
q_norm_out, _ = torch.ops.npu.npu_rms_norm(q_by_head, q_weight, self.eps)
|
||||
q_normed = q_norm_out + q_bias
|
||||
|
||||
k_by_head = k.view(*k.shape[:-1], k.shape[-1] // self.head_dim, self.head_dim)
|
||||
k_norm_out, _ = torch.ops.npu.npu_rms_norm(k_by_head, k_weight, self.eps)
|
||||
k_normed = k_norm_out + k_bias
|
||||
|
||||
q_flat = q_normed.view(q.shape)
|
||||
k_flat = k_normed.view(k.shape)
|
||||
q_rope, k_rope = torch.ops.vllm.npu_rotary_embedding(
|
||||
positions, q_flat, k_flat, cos_sin_cache, self.head_dim, self.rope_dim, True
|
||||
)
|
||||
|
||||
return q_rope, k_rope, v
|
||||
|
||||
return pattern
|
||||
|
||||
def get_replacement(self):
|
||||
def replacement(
|
||||
qkv: torch.Tensor,
|
||||
q_weight: torch.Tensor,
|
||||
k_weight: torch.Tensor,
|
||||
q_bias: torch.Tensor,
|
||||
k_bias: torch.Tensor,
|
||||
cos_sin_cache: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
):
|
||||
results = DeviceOperator.split_qkv_rmsnorm_rope(
|
||||
input=qkv,
|
||||
q_weight=q_weight,
|
||||
k_weight=k_weight,
|
||||
q_hidden_size=self.q_size,
|
||||
kv_hidden_size=self.kv_size,
|
||||
head_dim=self.head_dim,
|
||||
eps=self.eps,
|
||||
q_bias=q_bias,
|
||||
k_bias=k_bias,
|
||||
cos_sin_cache=cos_sin_cache,
|
||||
positions=positions,
|
||||
)
|
||||
return results
|
||||
|
||||
return replacement
|
||||
|
||||
|
||||
class QKNormRopeFusionPass(VllmInductorPass):
|
||||
"""
|
||||
A pass for fusing QKV split and RMSNorm operations into a single qk_rmsnorm operator.
|
||||
"""
|
||||
|
||||
def __init__(self, vllm_config: VllmConfig):
|
||||
super().__init__(vllm_config)
|
||||
self.pattern_match_passes: PatternMatcherPass = PatternMatcherPass(pass_name="qknorm_rope_fusion_pass")
|
||||
|
||||
dtype = vllm_config.model_config.dtype
|
||||
if dtype not in (torch.bfloat16,):
|
||||
logger.debug("QKNorm and Rope fusion not enabled: unsupported dtype %s", dtype)
|
||||
return
|
||||
|
||||
# use one attn layer to get meta (such as head_dim) for QKNormRopeFusionPattern
|
||||
attn_layers: dict[str, Attention] = get_layers_from_vllm_config(vllm_config, Attention)
|
||||
if len(attn_layers) == 0:
|
||||
logger.debug("QKNorm and Rope fusion enabled, but no Attention layers were discovered.")
|
||||
return
|
||||
layer = next(iter(attn_layers.values()))
|
||||
for epsilon in [1e-6, 1e-5]:
|
||||
if layer.head_size != 128:
|
||||
logger.debug("QKNorm and Rope fusion not enabled: head_dim %d is not equal of 128", layer.head_size)
|
||||
continue
|
||||
QKNormRopeFusionPattern(
|
||||
vllm_config=vllm_config,
|
||||
head_dim=layer.head_size,
|
||||
num_heads=layer.num_heads,
|
||||
num_kv_heads=layer.num_kv_heads,
|
||||
eps=epsilon,
|
||||
).register(self.pattern_match_passes)
|
||||
|
||||
QKNormRopeFusionPatternWithBias(
|
||||
vllm_config=vllm_config,
|
||||
head_dim=layer.head_size,
|
||||
num_heads=layer.num_heads,
|
||||
num_kv_heads=layer.num_kv_heads,
|
||||
eps=epsilon,
|
||||
).register(self.pattern_match_passes)
|
||||
|
||||
def __call__(self, graph: torch.fx.Graph):
|
||||
self.begin()
|
||||
self.matched_count = self.pattern_match_passes.apply(graph)
|
||||
logger.debug("Fused %s QKNorm and Rope patterns", self.matched_count)
|
||||
logger.debug("Patterns registered for replacement:")
|
||||
pattern_idx = 0
|
||||
for pattern_entry in self.pattern_match_passes.patterns.values():
|
||||
for p in pattern_entry:
|
||||
p_str = PatternPrettyPrinter.run(p.pattern)
|
||||
logger.debug("Pattern %d: %s", pattern_idx, p_str)
|
||||
pattern_idx += 1
|
||||
self.end_and_log()
|
||||
|
||||
def is_applicable_for_range(self, compile_range: Range) -> bool:
|
||||
"""
|
||||
Check if the pass is applicable for the current configuration.
|
||||
"""
|
||||
return True
|
||||
Reference in New Issue
Block a user