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Unverified Commit eab07f74 authored by Daniël de Kok's avatar Daniël de Kok Committed by GitHub
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Add support for FP8 KV cache scales (#2628) 

* Add support for FP8 KV cache scales

Since FP8 only has limited dynamic range, we can scale keys/values
before storing them into the cache (and unscale them in attention). To
avoid rescaling the cache as the absmax values change, good scales are
usually determined per layer using calibration calibration data and stored
in the checkpoint.

This change adds support for for using key-value scales and loading them
from checkpoints in the two most common formats:

- Separate per-layer `k_scale` and `v_scale` scalars.
- Per-layer `kv_scale` scalar (older format).

Currently, scales are only used with an `float8_e4m3fn` cache.

Besides adding support for key/value scales, the `fp8_quantize` function
is also extended to support quantization with a kernel vendored from
vLLM. This is slightly faster than the PyTorch implementation, but also
scales in FP32, potentially improving accuracy.

* Update FP8 KV cache test to use checkpoint with scales

* `can_scale`: check that the attention is flashinfer
parent 14a0df3a
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Showing with 360 additions and 141 deletions
flake.lock
View file @ eab07f74
......@@ -978,15 +978,16 @@
"nixpkgs": "nixpkgs_6"
},
"locked": {
"lastModified": 1728381423,
"narHash": "sha256-gpHy1WtlA8ZTd8XmxsdCoDd4Z7DE7co37lH7P+nsADA=",
"lastModified": 1729531056,
"narHash": "sha256-dW9IOA31+j3VS19WAWAmkJW2YCzeVZGqd6HpIJfODtI=",
"owner": "huggingface",
"repo": "text-generation-inference-nix",
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"rev": "a84a90281a17b15762873845c947e5c78f5a8dd1",
"type": "github"
},
"original": {
"owner": "huggingface",
"ref": "marlin-kernels-0.3.0",
"repo": "text-generation-inference-nix",
"type": "github"
}
......
flake.nix
View file @ eab07f74
......@@ -5,7 +5,7 @@
inputs.nixpkgs.follows = "tgi-nix/nixpkgs";
};
nix-filter.url = "github:numtide/nix-filter";
tgi-nix.url = "github:huggingface/text-generation-inference-nix";
tgi-nix.url = "github:huggingface/text-generation-inference-nix/marlin-kernels-0.3.0";
nixpkgs.follows = "tgi-nix/nixpkgs";
flake-utils.url = "github:numtide/flake-utils";
rust-overlay = {
......
integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache.json
View file @ eab07f74
......@@ -11,27 +11,27 @@
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......@@ -39,66 +39,66 @@
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integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_all_params.json
View file @ eab07f74
{
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......@@ -11,22 +11,22 @@
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......@@ -34,24 +34,66 @@
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"generated_text": "What is deep learning? \n "
"generated_text": "What is deep learning? \nDeep learning (DL) is a subfield"
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integration-tests/models/__snapshots__/test_flash_llama_fp8_kv_cache/test_flash_llama_fp8_kv_cache_load.json
View file @ eab07f74
......@@ -12,27 +12,27 @@
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}
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......@@ -40,68 +40,68 @@
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"generated_text": " Deep learning is a subset of machine learning that involves"
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......@@ -116,27 +116,27 @@
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"logprob": -6.21875,
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"text": " learning"
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......@@ -144,68 +144,68 @@
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"generated_text": " Deep learning is a subset of machine learning that involves"
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"details": {
......@@ -220,27 +220,27 @@
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"id": 3923,
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"logprob": -6.21875,
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{
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"logprob": -1.2265625,
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"text": " is"
},
{
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"logprob": -9.1015625,
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"text": " deep"
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"text": " learning"
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"logprob": -1.75,
"text": "?"
}
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......@@ -248,68 +248,68 @@
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"logprob": -1.1796875,
"special": false,
"text": " Deep"
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"generated_text": " Deep learning is a subset of machine learning that involves"
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"details": {
......@@ -324,27 +324,27 @@
},
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"logprob": -6.21875,
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{
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"logprob": -0.95703125,
"text": " is"
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"text": " learning"
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{
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"logprob": -1.75,
"text": "?"
}
],
......@@ -352,67 +352,67 @@
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"logprob": -1.1796875,
"special": false,
"text": " Deep"
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"logprob": -0.6953125,
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"text": " is"
"text": " involves"
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],
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"generated_text": " Deep learning is a subset of machine learning that is"
"generated_text": " Deep learning is a subset of machine learning that involves"
}
]
integration-tests/models/test_flash_llama_fp8_kv_cache.py
View file @ eab07f74
......@@ -4,7 +4,9 @@ import pytest
@pytest.fixture(scope="module")
def flash_llama_fp8_kv_cache_handle(launcher):
with launcher(
"meta-llama/Meta-Llama-3-8B", num_shard=2, kv_cache_dtype="fp8_e5m2"
"neuralmagic/Meta-Llama-3-8B-Instruct-FP8-KV",
num_shard=2,
kv_cache_dtype="fp8_e4m3fn",
) as handle:
yield handle
......@@ -25,7 +27,7 @@ async def test_flash_llama_fp8_kv_cache(flash_llama_fp8_kv_cache, response_snaps
assert (
response.generated_text
== " Deep learning is a subset of machine learning that is"
== " Deep learning is a subset of machine learning that involves"
)
assert response.details.generated_tokens == 10
assert response == response_snapshot
......@@ -69,7 +71,7 @@ async def test_flash_llama_fp8_kv_cache_load(
assert len(responses) == 4
assert (
responses[0].generated_text
== " Deep learning is a subset of machine learning that is"
== " Deep learning is a subset of machine learning that involves"
)
assert all(
[r.generated_text == responses[0].generated_text for r in responses]
......
server/poetry.lock
View file @ eab07f74
......@@ -1215,12 +1215,12 @@ files = [
[[package]]
name = "marlin-kernels"
version = "0.2.0"
version = "0.3.0"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:9a5afcf19b0f5917e43353cc19873fb3c4d4d0b924e2a95a37884f9ce208d0bd"},
{file = "marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", hash = "sha256:a2086b9e98d22071f52c5b4b4b98b1b4a988565258905173fa74c5a9eddd1a0a"},
]
[package.dependencies]
......@@ -1228,16 +1228,16 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl"
[[package]]
name = "marlin-kernels"
version = "0.2.0"
version = "0.3.0"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:1e64fcc7ebadfaffa60091ee9201ae3daaf5c1be3be60c8c054143a3dcb72d5d"},
{file = "marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", hash = "sha256:f39a6946d8247629446ec170832d832c7038c363f1d8803211fe67249c2d804d"},
]
[package.dependencies]
......@@ -1245,16 +1245,16 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl"
[[package]]
name = "marlin-kernels"
version = "0.2.0"
version = "0.3.0"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:e75f3ce9b1c13a4ed43a380d88e1d34d297259452db037ec1973ec33dc2eb78e"},
{file = "marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", hash = "sha256:07fd869d5289777fa866107dae676523e18b1f6ba4afce79946ddc58a6870169"},
]
[package.dependencies]
......@@ -1262,16 +1262,16 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl"
[[package]]
name = "marlin-kernels"
version = "0.2.0"
version = "0.3.0"
description = "Marlin quantization kernels"
optional = true
python-versions = ">=3.7"
files = [
{file = "marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:2f99a27f70b391887ee6adffeeee7c3f4df7fac37393f9fb16d4cace2b3f6457"},
{file = "marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", hash = "sha256:0dedaa418225d490a5f1d8f85dbc75e439a8c43a8870e4ef32945bf61672d7dc"},
]
[package.dependencies]
......@@ -1279,7 +1279,7 @@ torch = "*"
[package.source]
type = "url"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl"
[[package]]
name = "mdurl"
......
server/pyproject.toml
View file @ eab07f74
......@@ -41,10 +41,10 @@ py-cpuinfo = "^9.0.0"
numpy = "^1.26"
marlin-kernels = [
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.2.0/marlin_kernels-0.2.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp310-cp310-linux_x86_64.whl", python = "~3.10", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp311-cp311-linux_x86_64.whl", python = "~3.11", optional = true },
{ url = "https://github.com/danieldk/marlin-kernels/releases/download/v0.3.0/marlin_kernels-0.3.0+cu123torch2.4-cp312-cp312-linux_x86_64.whl", python = "~3.12", optional = true },
]
moe-kernels = [
{ url = "https://github.com/danieldk/moe-kernels/releases/download/v0.6.0/moe_kernels-0.6.0+cu123torch2.4-cp39-cp39-linux_x86_64.whl", python = "~3.9", optional = true },
......
server/text_generation_server/layers/attention/__init__.py
View file @ eab07f74
......@@ -28,10 +28,11 @@ else:
raise ImportError(f"System {SYSTEM} doesn't support flash/paged attention")
# KVCache needs `reshape_and_cache`, so ensure that it is defined already.
from .kv_cache import KVCache
from .kv_cache import KVCache, get_kv_scales
__all__ = [
"attention",
"get_kv_scales",
"paged_attention",
"SUPPORTS_WINDOWING",
"KVCache",
......
server/text_generation_server/layers/attention/cuda.py
View file @ eab07f74
import torch
from text_generation_server.layers.attention.kv_cache import KVCache
from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.models.globals import (
ATTENTION,
......@@ -8,6 +8,7 @@ from text_generation_server.models.globals import (
from text_generation_server.layers.attention import Seqlen
from typing import Optional
major, minor = torch.cuda.get_device_capability()
is_sm75 = major == 7 and minor == 5
_PARTITION_SIZE = 512
......@@ -21,6 +22,8 @@ def paged_attention(
block_tables: torch.Tensor,
seqlen: Seqlen,
max_s: int,
*,
kv_scales: KVScales,
softcap: Optional[float] = None,
):
# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
......@@ -46,6 +49,8 @@ def paged_attention(
num_seqs, num_heads, head_size = query.shape
max_num_partitions = (max_s + _PARTITION_SIZE - 1) // _PARTITION_SIZE
can_scale = kv_cache.can_scale(kv_scales)
# NOTE(woosuk): We use a simple heuristic to decide whether to use
# PagedAttention V1 or V2. If the number of partitions is 1, we use
# V1 to avoid the overhead of reduction. Also, if the number of
......@@ -60,6 +65,8 @@ def paged_attention(
paged_kv_cache=(kv_cache.key, kv_cache.value),
logits_soft_cap=softcap,
sm_scale=softmax_scale,
k_scale=kv_scales.key_scale_cpu if can_scale else 1.0,
v_scale=kv_scales.value_scale_cpu if can_scale else 1.0,
)
elif ATTENTION == "flashdecoding":
max_q = 1
......@@ -205,6 +212,7 @@ def attention(
key: torch.Tensor,
value: torch.Tensor,
kv_cache: KVCache,
kv_scales: KVScales,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale: float,
......@@ -212,6 +220,8 @@ def attention(
causal: bool = True,
softcap: Optional[float] = None,
):
can_scale = kv_cache.can_scale(kv_scales)
if ATTENTION == "flashinfer":
from text_generation_server.layers.attention.flashinfer import (
prefill_with_paged_kv_state,
......@@ -228,6 +238,8 @@ def attention(
logits_soft_cap=softcap,
sm_scale=softmax_scale,
window_left=window_size_left,
k_scale=kv_scales.key_scale_cpu if can_scale else 1.0,
v_scale=kv_scales.value_scale_cpu if can_scale else 1.0,
)
# If we are using flashdecoding or paged, we always use flash-attn for
......
server/text_generation_server/layers/attention/flashinfer.py
View file @ eab07f74
......@@ -204,6 +204,7 @@ def use_decode_state(
num_kv_heads: int,
head_size: int,
page_size: int,
kv_cache_dtype: torch.dtype,
dtype: torch.dtype,
window_left: int,
):
......@@ -240,7 +241,7 @@ def use_decode_state(
num_kv_heads=num_kv_heads,
head_dim=head_size,
page_size=page_size,
data_type=dtype,
data_type=kv_cache_dtype,
q_data_type=dtype,
window_left=window_left,
)
......
server/text_generation_server/layers/attention/ipex.py
View file @ eab07f74
import intel_extension_for_pytorch as ipex
import torch
from text_generation_server.layers.attention.kv_cache import KVCache
from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from text_generation_server.models.flash_causal_lm import BLOCK_SIZE
from text_generation_server.layers.attention import Seqlen
from typing import Optional
......@@ -14,6 +14,7 @@ def attention(
key: torch.Tensor,
value: torch.Tensor,
kv_cache: KVCache,
kv_scales: KVScales,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale: float,
......@@ -55,6 +56,8 @@ def paged_attention(
block_tables: torch.Tensor,
seqlen: Seqlen,
max_s: int,
*,
kv_scales: KVScales,
softcap: Optional[float] = None,
):
if softcap is not None:
......
server/text_generation_server/layers/attention/kv_cache.py
View file @ eab07f74
from typing import Tuple
from dataclasses import dataclass, field
from loguru import logger
import torch
from text_generation_server.layers.fp8 import fp8_quantize
from text_generation_server.models.globals import ATTENTION, BLOCK_SIZE
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.utils.log import log_once
from text_generation_server.utils.weights import Weights
@dataclass
class KVScales:
"""
Key-value scales for FP8 KV cache.
This data class stores key and value scales both as a GPU tensor and
as a GPU float. This inconvenience is necessary because some functions
(e.g. scaling kernels) take scales as a GPU tensor, whereas others
(e.g. flashinfer) take scales as a CPU scalar.
"""
key_scale: torch.Tensor
value_scale: torch.Tensor
key_scale_cpu: float = field(init=False)
value_scale_cpu: float = field(init=False)
def __post_init__(self):
if self.key_scale.numel() != 1 or self.value_scale.numel() != 1:
raise ValueError("Key and value scales must be scalar tensors.")
self.key_scale_cpu = self.key_scale.item()
self.value_scale_cpu = self.value_scale.item()
class KVCache:
......@@ -76,6 +106,33 @@ class KVCache:
),
)
def can_scale(self, kv_scales: KVScales) -> bool:
"""Check if the cache can be scaled by the given scales."""
if kv_scales.key_scale_cpu == 1.0 and kv_scales.value_scale_cpu == 1.0:
return False
elif (
self.dtype == torch.float8_e4m3fn
and ATTENTION == "flashinfer"
and SYSTEM == "cuda"
):
log_once(
logger.info,
"Using FP8 KV cache scales",
)
return True
else:
# We have scales, but not the correct FP8 cache type, so warn once.
log_once(
logger.info,
"Ignoring FP8 KV cache scales, only float8_e4m3fn KV cache on flashinfer is supported",
)
return False
@property
def dtype(self):
"""Get the data type of the cache."""
return self.kv_cache[0].dtype
@property
def key(self):
"""Get the key cache."""
......@@ -94,17 +151,33 @@ class KVCache:
key: torch.Tensor,
value: torch.Tensor,
slots: torch.Tensor,
kv_scales: KVScales,
):
"""Store the key and value at the given slots."""
key_cache = self.kv_cache[0]
value_cache = self.kv_cache[1]
if self.can_scale(kv_scales):
if kv_scales.key_scale_cpu != 1.0:
key = fp8_quantize(
key.float(),
scale=kv_scales.key_scale,
qdtype=self.dtype,
scalar=True,
)[0]
if kv_scales.value_scale_cpu != 1.0:
value = fp8_quantize(
value.float(),
scale=kv_scales.value_scale,
qdtype=self.dtype,
scalar=True,
)[0]
if ATTENTION in {"flashdecoding", "flashinfer"}:
# TODO: add scale
key = key.to(key_cache.dtype)
value = value.to(value_cache.dtype)
if key_cache.dtype in {torch.float8_e5m2, torch.float8_e4m3fn}:
if key_cache.dtype in {torch.float8_e4m3fn, torch.float8_e5m2}:
# Torch index_put does not support float8_{e5m2,e4m3fn} yet, so
# put as raw data instead.
key_cache = key_cache.view(torch.uint8)
......@@ -151,5 +224,23 @@ def paged_reshape_and_cache(
)
else:
raise NotImplementedError(
f"Cannot reshape and cache for paged attention, system '{SYSTEM}' not supportedattention"
f"Cannot reshape and cache for paged attention, system '{SYSTEM}' not supported"
)
def get_kv_scales(weights: Weights, prefix: str) -> KVScales:
"""Load KV cache scales."""
key_scale = torch.tensor(1.0, dtype=torch.float32, device=weights.device)
value_scale = key_scale
if weights.has_tensor(f"{prefix}.k_scale") and weights.has_tensor(
f"{prefix}.v_scale"
):
key_scale = weights.get_tensor(f"{prefix}.k_scale", to_dtype=False).float()
value_scale = weights.get_tensor(f"{prefix}.v_scale", to_dtype=False).float()
elif weights.has_tensor(f"{prefix}.kv_scale"):
# Fall back to older more coarse-grained scale when available.
key_scale = weights.get_tensor(f"{prefix}.kv_scale").float()
value_scale = key_scale
return KVScales(key_scale=key_scale, value_scale=value_scale)
server/text_generation_server/layers/attention/rocm.py
View file @ eab07f74
import os
from typing import Optional
import torch
from text_generation_server.layers.attention.kv_cache import KVCache
from text_generation_server.layers.attention.kv_cache import KVCache, KVScales
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers.attention import Seqlen
from text_generation_server.utils.log import log_master
......@@ -36,6 +36,8 @@ def paged_attention(
block_tables: torch.Tensor,
seqlen: Seqlen,
max_s: int,
*,
kv_scales: KVScales,
softcap: Optional[float] = None,
):
# Adapted from: https://github.com/vllm-project/vllm/blob/f8a1e39fae05ca610be8d5a78be9d40f5274e5fc/vllm/model_executor/layers/attention.py
......@@ -210,6 +212,7 @@ def attention(
key: torch.Tensor,
value: torch.Tensor,
kv_cache: KVCache,
kv_scales: KVScales,
seqlen: Seqlen,
block_tables: torch.Tensor,
softmax_scale: float,
......
server/text_generation_server/layers/fp8.py
View file @ eab07f74
......@@ -26,6 +26,12 @@ def is_fbgemm_gpu_available():
return False
try:
import marlin_kernels
except ImportError:
marlin_kernels = None
if is_fbgemm_gpu_available():
if SYSTEM == "cuda":
major, _ = torch.cuda.get_device_capability()
......@@ -94,6 +100,17 @@ def fp8_quantize(
)
return qweight, scale
if marlin_kernels is not None:
shape = weight.shape
qweight, scale = marlin_kernels.scaled_fp8_quant(
weight.reshape(-1, shape[-1]),
dtype=qdtype,
scale=scale,
scale_ub=scale_upper_bound,
)
return qweight.reshape(shape), scale
# weight, scale = quant_weights(weight, torch.int8, False)
finfo = torch.finfo(qdtype)
......
server/text_generation_server/models/custom_modeling/flash_cohere_modeling.py
View file @ eab07f74
......@@ -30,6 +30,7 @@ from text_generation_server.layers.attention import (
attention,
Seqlen,
)
from text_generation_server.layers.attention.kv_cache import get_kv_scales
from text_generation_server.utils.import_utils import SYSTEM
from text_generation_server.layers import (
TensorParallelRowLinear,
......@@ -227,6 +228,7 @@ class FlashCohereAttention(torch.nn.Module):
)
self.query_key_value = load_attention(config, prefix, weights)
self.kv_scales = get_kv_scales(weights, f"{prefix}")
self.use_qk_norm = config.use_qk_norm
if self.use_qk_norm:
......@@ -289,7 +291,12 @@ class FlashCohereAttention(torch.nn.Module):
self.rotary_emb(query, key, cos, sin)
kv_cache.store(key=key, value=value, slots=slots)
kv_cache.store(
key=key,
value=value,
slots=slots,
kv_scales=self.kv_scales,
)
# Prefill
if cu_seqlen_prefill is not None:
......@@ -299,6 +306,7 @@ class FlashCohereAttention(torch.nn.Module):
key=key,
value=value,
kv_cache=kv_cache,
kv_scales=self.kv_scales,
seqlen=seqlen,
block_tables=block_tables,
softmax_scale=self.softmax_scale,
......@@ -313,6 +321,7 @@ class FlashCohereAttention(torch.nn.Module):
block_tables,
seqlen,
max_s,
kv_scales=self.kv_scales,
)
return self.o_proj(
......
server/text_generation_server/models/custom_modeling/flash_dbrx_modeling.py
View file @ eab07f74
......@@ -20,6 +20,7 @@ from torch import nn
from transformers.activations import ACT2FN
from transformers.configuration_utils import PretrainedConfig
from typing import Optional, List, Tuple, Any
from text_generation_server.layers.attention.kv_cache import get_kv_scales
from text_generation_server.utils.import_utils import SYSTEM
if SYSTEM != "ipex":
......@@ -288,6 +289,7 @@ class DbrxAttention(torch.nn.Module):
)
self.query_key_value = load_attention(config, prefix, weights)
self.kv_scales = get_kv_scales(weights, f"{prefix}")
self.o_proj = TensorParallelRowLinear.load(
config,
......@@ -328,7 +330,12 @@ class DbrxAttention(torch.nn.Module):
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
kv_cache.store(
key=kv[:, 0],
value=kv[:, 1],
slots=slots,
kv_scales=self.kv_scales,
)
# Prefill
if cu_seqlen_prefill is not None:
......@@ -338,6 +345,7 @@ class DbrxAttention(torch.nn.Module):
key=kv[:, 0],
value=kv[:, 1],
kv_cache=kv_cache,
kv_scales=self.kv_scales,
seqlen=seqlen,
block_tables=block_tables,
softmax_scale=self.softmax_scale,
......@@ -352,6 +360,7 @@ class DbrxAttention(torch.nn.Module):
block_tables,
seqlen,
max_s,
kv_scales=self.kv_scales,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
......
server/text_generation_server/models/custom_modeling/flash_deepseek_v2_modeling.py
View file @ eab07f74
......@@ -34,6 +34,7 @@ from text_generation_server.layers.attention import (
attention,
paged_attention,
)
from text_generation_server.layers.attention.kv_cache import KVCache, get_kv_scales
from text_generation_server.layers.layernorm import FastRMSNorm
from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
from text_generation_server.layers.rotary import PositionRotaryEmbedding, get_mscale
......@@ -230,6 +231,8 @@ class DeepseekV2Attention(torch.nn.Module):
),
)
self.kv_scales = get_kv_scales(weights, f"{prefix}")
self.kv_a_layernorm = FastRMSNorm.load(
prefix=f"{prefix}.kv_a_layernorm", weights=weights, eps=config.rms_norm_eps
)
......@@ -258,7 +261,7 @@ class DeepseekV2Attention(torch.nn.Module):
cos: torch.Tensor,
sin: torch.Tensor,
cu_seqlen_prefill: torch.Tensor,
kv_cache: Tuple[torch.Tensor, torch.Tensor],
kv_cache: KVCache,
block_tables: torch.Tensor,
slots: torch.Tensor,
seqlen: Seqlen,
......@@ -319,7 +322,12 @@ class DeepseekV2Attention(torch.nn.Module):
value, (0, self.head_pad_size - self.value_head_size), value=0
)
kv_cache.store(key=key, value=value, slots=slots)
kv_cache.store(
key=key,
value=value,
slots=slots,
kv_scales=self.kv_scales,
)
# Prefill
if cu_seqlen_prefill is not None:
......@@ -329,6 +337,7 @@ class DeepseekV2Attention(torch.nn.Module):
key=key,
value=value,
kv_cache=kv_cache,
kv_scales=self.kv_scales,
seqlen=seqlen,
block_tables=block_tables,
softmax_scale=self.softmax_scale,
......@@ -343,6 +352,7 @@ class DeepseekV2Attention(torch.nn.Module):
block_tables,
seqlen,
max_s,
kv_scales=self.kv_scales,
)
# Remove padding.
......
server/text_generation_server/models/custom_modeling/flash_gemma2_modeling.py
View file @ eab07f74
......@@ -39,6 +39,7 @@ from text_generation_server.layers import (
TensorParallelMultiAdapterLinear,
TensorParallelAdapterRowLinear,
)
from text_generation_server.layers.attention.kv_cache import get_kv_scales
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
......@@ -206,6 +207,7 @@ class FlashGemma2Attention(torch.nn.Module):
],
process_group=weights.process_group,
)
self.kv_scales = get_kv_scales(weights, f"{prefix}")
o_proj = TensorParallelRowLinear.load(
config,
......@@ -251,7 +253,12 @@ class FlashGemma2Attention(torch.nn.Module):
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
kv_cache.store(
key=kv[:, 0],
value=kv[:, 1],
slots=slots,
kv_scales=self.kv_scales,
)
# Prefill
if cu_seqlen_prefill is not None:
......@@ -261,6 +268,7 @@ class FlashGemma2Attention(torch.nn.Module):
key=kv[:, 0],
value=kv[:, 1],
kv_cache=kv_cache,
kv_scales=self.kv_scales,
seqlen=seqlen,
block_tables=block_tables,
softmax_scale=self.softmax_scale,
......@@ -278,6 +286,7 @@ class FlashGemma2Attention(torch.nn.Module):
seqlen,
max_s,
softcap=self.softcap,
kv_scales=self.kv_scales,
)
return self.o_proj(
......
server/text_generation_server/models/custom_modeling/flash_gemma_modeling.py
View file @ eab07f74
......@@ -37,6 +37,7 @@ from text_generation_server.layers import (
SpeculativeHead,
get_linear,
)
from text_generation_server.layers.attention.kv_cache import get_kv_scales
from text_generation_server.layers.rotary import PositionRotaryEmbedding
from text_generation_server.layers.layernorm import (
FastRMSNorm,
......@@ -185,6 +186,7 @@ class FlashGemmaAttention(torch.nn.Module):
)
self.query_key_value = load_attention(config, prefix, weights)
self.kv_scales = get_kv_scales(weights, f"{prefix}")
self.o_proj = TensorParallelRowLinear.load(
config,
......@@ -222,7 +224,12 @@ class FlashGemmaAttention(torch.nn.Module):
self.rotary_emb(query, torch.select(kv, dim=1, index=0), cos, sin)
kv_cache.store(key=kv[:, 0], value=kv[:, 1], slots=slots)
kv_cache.store(
key=kv[:, 0],
value=kv[:, 1],
slots=slots,
kv_scales=self.kv_scales,
)
# Prefill
if cu_seqlen_prefill is not None:
......@@ -232,6 +239,7 @@ class FlashGemmaAttention(torch.nn.Module):
key=kv[:, 0],
value=kv[:, 1],
kv_cache=kv_cache,
kv_scales=self.kv_scales,
seqlen=seqlen,
block_tables=block_tables,
softmax_scale=self.softmax_scale,
......@@ -247,6 +255,7 @@ class FlashGemmaAttention(torch.nn.Module):
block_tables,
seqlen,
max_s,
kv_scales=self.kv_scales,
)
return self.o_proj(attn_output.view(-1, self.num_heads * self.head_size))
......
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