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[ROCM] DSfp4 mla projection gemms weight dynamic quantization (#32238)

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Signed-off-by: Aleksandr Malyshev <maleksan@amd.com>
Co-authored-by: Aleksandr Malyshev <maleksan@amd.com>
This commit is contained in:
Aleksandr Malyshev
2026-01-15 12:13:08 -08:00
committed by GitHub
parent bd292be0c0
commit 8c11001ba2
4 changed files with 97 additions and 80 deletions
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30
vllm/_aiter_ops.py
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@@ -837,6 +837,7 @@ class rocm_aiter_ops:
_TRITON_UNIFIED_ATTN_ENABLED = envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
# TODO: Consolidate under _LINEAR_ENABLED
_FP8BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP8BMM
_FP4BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP4BMM
# TODO: Consolidate under _LINEAR_ENABLED
_FP4_GEMM_DYNAMIC_QUANT_ASM = envs.VLLM_ROCM_USE_AITER_FP4_ASM_GEMM
# TODO: Consolidate under VLLM_ROCM_USE_AITER_ROPE
@@ -863,6 +864,7 @@ class rocm_aiter_ops:
cls._SHUFFLE_KV_CACHE_ENABLED = envs.VLLM_ROCM_SHUFFLE_KV_CACHE_LAYOUT
cls._TRITON_UNIFIED_ATTN_ENABLED = envs.VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION
cls._FP8BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP8BMM
cls._FP4BMM_ENABLED = envs.VLLM_ROCM_USE_AITER_FP4BMM
cls._FP4_GEMM_DYNAMIC_QUANT_ASM = envs.VLLM_ROCM_USE_AITER_FP4_ASM_GEMM
cls._TRITON_ROTARY_EMBED = envs.VLLM_ROCM_USE_AITER_TRITON_ROPE
cls._MOE_SHARED_EXPERTS_ENABLED = envs.VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS
@@ -923,6 +925,11 @@ class rocm_aiter_ops:
def is_fp8bmm_enabled(cls) -> bool:
return cls._AITER_ENABLED and cls._FP8BMM_ENABLED
@classmethod
@if_aiter_supported
def is_fp4bmm_enabled(cls) -> bool:
return cls._AITER_ENABLED and cls._FP4BMM_ENABLED
@classmethod
@if_aiter_supported
def is_asm_fp4_gemm_dynamic_quant_enabled(cls) -> bool:
@@ -1403,6 +1410,29 @@ class rocm_aiter_ops:
query = query.view(query_shape)
key = key.view(key_shape)
@staticmethod
def batched_gemm_a16wfp4(
X: torch.Tensor,
W: torch.Tensor,
w_scale: torch.Tensor,
Y: torch.Tensor,
transpose_bm: bool | None = False,
prequant: bool | None = False,
y_scale: torch.Tensor | None = None,
) -> torch.Tensor:
# ruff: noqa: E501 # isort: skip
from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4
return batched_gemm_a16wfp4(
X,
W,
w_scale,
y=Y,
transpose_bm=transpose_bm,
prequant=prequant,
y_scale=y_scale,
)
@staticmethod
def triton_fp8_bmm(
X: torch.Tensor,

6
vllm/envs.py
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@@ -121,6 +121,7 @@ if TYPE_CHECKING:
VLLM_ROCM_USE_AITER_FP4_ASM_GEMM: bool = False
VLLM_ROCM_USE_AITER_TRITON_ROPE: bool = False
VLLM_ROCM_USE_AITER_FP8BMM: bool = True
VLLM_ROCM_USE_AITER_FP4BMM: bool = True
VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION: bool = False
VLLM_ROCM_USE_AITER_FUSION_SHARED_EXPERTS: bool = False
VLLM_ROCM_USE_AITER_TRITON_GEMM: bool = True
@@ -991,6 +992,11 @@ environment_variables: dict[str, Callable[[], Any]] = {
"VLLM_ROCM_USE_AITER_FP8BMM": lambda: (
os.getenv("VLLM_ROCM_USE_AITER_FP8BMM", "True").lower() in ("true", "1")
),
# Whether to use aiter triton fp4 bmm kernel
# By default is enabled.
"VLLM_ROCM_USE_AITER_FP4BMM": lambda: (
os.getenv("VLLM_ROCM_USE_AITER_FP4BMM", "True").lower() in ("true", "1")
),
# Use AITER triton unified attention for V1 attention
"VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION": lambda: (
os.getenv("VLLM_ROCM_USE_AITER_UNIFIED_ATTENTION", "False").lower()

127
vllm/model_executor/layers/attention/mla_attention.py
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@@ -1183,6 +1183,12 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
self.q_pad_num_heads = q_pad_num_heads
self.is_aiter_triton_fp8_bmm_enabled = rocm_aiter_ops.is_fp8bmm_enabled()
# If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
self.is_aiter_triton_fp4_bmm_enabled = (
rocm_aiter_ops.is_fp4bmm_enabled()
and self.kv_b_proj.weight.dtype == torch.bfloat16
)
def process_weights_after_loading(self, act_dtype: torch.dtype):
# we currently do not have quantized bmm's which are needed for
# `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform
@@ -1211,7 +1217,21 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
[self.qk_nope_head_dim, self.v_head_dim], dim=-1
)
if self.is_aiter_triton_fp8_bmm_enabled:
# If kv_b_proj_weight is unquantized, quantize it to mxfp4 if supported
if self.is_aiter_triton_fp4_bmm_enabled:
from vllm.model_executor.layers.quantization.quark.utils import (
quark_quantize_weight_to_mxfp4,
)
self.W_K, self.W_K_scale = quark_quantize_weight_to_mxfp4(W_UK)
# Convert from (L, N, P) to (N, L, P)
self.W_K = self.W_K.transpose(0, 1)
self.W_K_scale = self.W_K_scale.transpose(0, 1)
self.W_V, self.W_V_scale = quark_quantize_weight_to_mxfp4(
W_UV.permute(1, 2, 0)
)
elif self.is_aiter_triton_fp8_bmm_enabled:
W_K = W_UK.transpose(0, 1) # 16 512 128
W_V = W_UV.permute(1, 2, 0) # 16 128 512
self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
@@ -1261,16 +1281,26 @@ class MLACommonBaseImpl(MLAAttentionImpl[A], Generic[A]):
def _v_up_proj(self, x: torch.Tensor, out: torch.Tensor):
# Convert from (B, N, L) to (N, B, L)
x = x.view(-1, self.num_heads, self.kv_lora_rank).transpose(0, 1)
if self.is_aiter_triton_fp8_bmm_enabled:
out = out.view(-1, self.num_heads, self.v_head_dim)
out = out.view(-1, self.num_heads, self.v_head_dim)
if self.is_aiter_triton_fp4_bmm_enabled:
out = rocm_aiter_ops.batched_gemm_a16wfp4(
x,
self.W_V,
self.W_V_scale,
out,
transpose_bm=True,
prequant=True,
y_scale=None,
)
x = out.view(-1, self.num_heads * self.v_head_dim)
elif self.is_aiter_triton_fp8_bmm_enabled:
# Multiply + Transpose (N, B, L) x (N, L, V)->(N, B, V)->(B, N, V)
x = rocm_aiter_ops.triton_fp8_bmm(
x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True, YQ=out
)
else:
# Convert from (B, N * V) to (N, B, V)
out = out.view(-1, self.num_heads, self.v_head_dim).transpose(0, 1)
out = out.transpose(0, 1)
# Multiply (N, B, L) x (N, L, V) -> (N, B, V)
torch.bmm(x, self.W_UV, out=out) # Reuse "out" to make it "hot"
@@ -1579,80 +1609,6 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
# Convert from (q_len, num_heads) to (num_heads, q_len)
return attn_out, lse.transpose(0, 1).contiguous()
def process_weights_after_loading(self, act_dtype: torch.dtype):
# we currently do not have quantized bmm's which are needed for
# `W_UV` and `W_UK_T`, we just store fp16/bf16 copies and perform
# the bmm's in 16-bit, the extra memory overhead of this is fairly low
kv_b_proj_weight = get_and_maybe_dequant_weights(
self.kv_b_proj, out_dtype=act_dtype
).T
assert kv_b_proj_weight.shape == (
self.kv_lora_rank,
self.num_heads * (self.qk_nope_head_dim + self.v_head_dim),
), (
f"{kv_b_proj_weight.shape=}, "
f"{self.kv_lora_rank=}, "
f"{self.num_heads=}, "
f"{self.qk_nope_head_dim=}, "
f"{self.v_head_dim=}"
)
kv_b_proj_weight = kv_b_proj_weight.view(
self.kv_lora_rank,
self.num_heads,
self.qk_nope_head_dim + self.v_head_dim,
)
W_UK, W_UV = kv_b_proj_weight.split(
[self.qk_nope_head_dim, self.v_head_dim], dim=-1
)
if self.is_aiter_triton_fp8_bmm_enabled:
W_K = W_UK.transpose(0, 1) # 16 512 128
W_V = W_UV.permute(1, 2, 0) # 16 128 512
self.W_K, self.W_K_scale = dynamic_per_batched_tensor_quant(
W_K, dtype=current_platform.fp8_dtype()
)
self.W_V, self.W_V_scale = dynamic_per_batched_tensor_quant(
W_V, dtype=current_platform.fp8_dtype()
)
# The kernel operates on non-padded inputs. Hence, pre-compiling
# triton kernel to avoid runtime compilation for unseen batch sizes
# Pre-compile for batch sizes 1 to 1024 to cover most use-cases.
# On DS-R1, this step adds roughly 50s to the model loading time.
max_batch_size = 1024 # [ToDo] Find the optimal upper limit
pre_compilation_list = list(range(1, max_batch_size + 1))
if is_global_first_rank():
pre_compilation_list = tqdm(
pre_compilation_list,
desc="[Aiter Triton] Pre-compiling fp8 BMM kernel",
total=max_batch_size,
)
for m in pre_compilation_list:
x = torch.empty(
(self.W_K.shape[0], m, self.W_K.shape[2]),
dtype=torch.bfloat16,
device=self.W_K.device,
)
rocm_aiter_ops.triton_fp8_bmm(
x, self.W_K, self.W_K_scale, group_size=128, transpose_bm=True
)
x = torch.empty(
(self.W_V.shape[0], m, self.W_V.shape[2]),
dtype=torch.bfloat16,
device=self.W_V.device,
)
rocm_aiter_ops.triton_fp8_bmm(
x, self.W_V, self.W_V_scale, group_size=128, transpose_bm=True
)
else:
# Convert from (L, N, V) to (N, L, V)
self.W_UV = W_UV.transpose(0, 1)
# Convert from (L, N, P) to (N, P, L)
self.W_UK_T = W_UK.permute(1, 2, 0)
def _concat_k_nope_k_pe(
self, k_nope: torch.Tensor, k_pe: torch.Tensor
) -> torch.Tensor:
@@ -2033,7 +1989,18 @@ class MLACommonImpl(MLACommonBaseImpl[M], Generic[M]):
decode_pe_padded.copy_(decode_q_pe)
decode_q_pe = decode_pe_padded
if self.is_aiter_triton_fp8_bmm_enabled:
if self.is_aiter_triton_fp4_bmm_enabled:
from aiter.ops.triton.batched_gemm_a16wfp4 import batched_gemm_a16wfp4
decode_ql_nope = batched_gemm_a16wfp4(
decode_q_nope,
self.W_K,
self.W_K_scale,
transpose_bm=True,
prequant=True,
y_scale=layer._q_scale if fp8_attention else None,
)
elif self.is_aiter_triton_fp8_bmm_enabled:
# Multiply+Transpose (N, B, P)x(N, P, L)->(N, B, L)->(B, N, L)
decode_ql_nope = rocm_aiter_ops.triton_fp8_bmm(
decode_q_nope,

14
vllm/model_executor/layers/quantization/quark/utils.py
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@@ -6,6 +6,7 @@ from types import MappingProxyType
from typing import Any
import regex as re
import torch
def deep_compare(dict1: Any, dict2: Any) -> bool:
@@ -103,3 +104,16 @@ def _is_equal_or_regex_match(
elif target == value:
return True
return False
# utility for tensor dims > 2 cases
def quark_quantize_weight_to_mxfp4(w: torch.Tensor):
assert w.dtype == torch.bfloat16, (
"Quark dynamic quantization is supported only for fp16 weights and only to MXF4"
)
from aiter.ops.triton.quant import dynamic_mxfp4_quant
*dims, d = w.shape
w, w_scales = dynamic_mxfp4_quant(w.reshape(-1, d))
return w.view(*dims, d // 2), w_scales.view(*dims, d // 32)