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
This commit is contained in:
Daniël de Kok
GitHub
parent
14a0df3a38
commit
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"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 30,
|
||||
"logprob": -1.0439453,
|
||||
"logprob": -1.75,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
|
|
@ -248,68 +248,68 @@
|
|||
"tokens": [
|
||||
{
|
||||
"id": 18682,
|
||||
"logprob": -2.1992188,
|
||||
"logprob": -1.1796875,
|
||||
"special": false,
|
||||
"text": " Deep"
|
||||
},
|
||||
{
|
||||
"id": 6975,
|
||||
"logprob": -0.07897949,
|
||||
"logprob": -0.005432129,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 374,
|
||||
"logprob": -0.27734375,
|
||||
"logprob": -0.02758789,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 264,
|
||||
"logprob": -0.37402344,
|
||||
"logprob": -0.013366699,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 27084,
|
||||
"logprob": -1.4511719,
|
||||
"logprob": -0.6953125,
|
||||
"special": false,
|
||||
"text": " subset"
|
||||
},
|
||||
{
|
||||
"id": 315,
|
||||
"logprob": -0.02909851,
|
||||
"logprob": -0.0004863739,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 5780,
|
||||
"logprob": -0.25854492,
|
||||
"logprob": -0.02709961,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
},
|
||||
{
|
||||
"id": 6975,
|
||||
"logprob": -0.0061798096,
|
||||
"logprob": -0.00022506714,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 430,
|
||||
"logprob": -1.3046875,
|
||||
"logprob": -0.19726562,
|
||||
"special": false,
|
||||
"text": " that"
|
||||
},
|
||||
{
|
||||
"id": 374,
|
||||
"logprob": -1.5537109,
|
||||
"id": 18065,
|
||||
"logprob": -0.77734375,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
"text": " involves"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": " Deep learning is a subset of machine learning that is"
|
||||
"generated_text": " Deep learning is a subset of machine learning that involves"
|
||||
},
|
||||
{
|
||||
"details": {
|
||||
|
|
@ -324,27 +324,27 @@
|
|||
},
|
||||
{
|
||||
"id": 3923,
|
||||
"logprob": -5.6328125,
|
||||
"logprob": -6.21875,
|
||||
"text": "What"
|
||||
},
|
||||
{
|
||||
"id": 374,
|
||||
"logprob": -1.2265625,
|
||||
"logprob": -0.95703125,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 5655,
|
||||
"logprob": -9.1015625,
|
||||
"logprob": -9.9375,
|
||||
"text": " deep"
|
||||
},
|
||||
{
|
||||
"id": 6975,
|
||||
"logprob": -1.8085938,
|
||||
"logprob": -1.1328125,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 30,
|
||||
"logprob": -1.0439453,
|
||||
"logprob": -1.75,
|
||||
"text": "?"
|
||||
}
|
||||
],
|
||||
|
|
@ -352,67 +352,67 @@
|
|||
"tokens": [
|
||||
{
|
||||
"id": 18682,
|
||||
"logprob": -2.1992188,
|
||||
"logprob": -1.1796875,
|
||||
"special": false,
|
||||
"text": " Deep"
|
||||
},
|
||||
{
|
||||
"id": 6975,
|
||||
"logprob": -0.07897949,
|
||||
"logprob": -0.005432129,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 374,
|
||||
"logprob": -0.27734375,
|
||||
"logprob": -0.02758789,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
},
|
||||
{
|
||||
"id": 264,
|
||||
"logprob": -0.37402344,
|
||||
"logprob": -0.013366699,
|
||||
"special": false,
|
||||
"text": " a"
|
||||
},
|
||||
{
|
||||
"id": 27084,
|
||||
"logprob": -1.4511719,
|
||||
"logprob": -0.6953125,
|
||||
"special": false,
|
||||
"text": " subset"
|
||||
},
|
||||
{
|
||||
"id": 315,
|
||||
"logprob": -0.02909851,
|
||||
"logprob": -0.0004863739,
|
||||
"special": false,
|
||||
"text": " of"
|
||||
},
|
||||
{
|
||||
"id": 5780,
|
||||
"logprob": -0.25854492,
|
||||
"logprob": -0.02709961,
|
||||
"special": false,
|
||||
"text": " machine"
|
||||
},
|
||||
{
|
||||
"id": 6975,
|
||||
"logprob": -0.0061798096,
|
||||
"logprob": -0.00022506714,
|
||||
"special": false,
|
||||
"text": " learning"
|
||||
},
|
||||
{
|
||||
"id": 430,
|
||||
"logprob": -1.3046875,
|
||||
"logprob": -0.19726562,
|
||||
"special": false,
|
||||
"text": " that"
|
||||
},
|
||||
{
|
||||
"id": 374,
|
||||
"logprob": -1.5537109,
|
||||
"id": 18065,
|
||||
"logprob": -0.77734375,
|
||||
"special": false,
|
||||
"text": " is"
|
||||
"text": " involves"
|
||||
}
|
||||
],
|
||||
"top_tokens": null
|
||||
},
|
||||
"generated_text": " Deep learning is a subset of machine learning that is"
|
||||
"generated_text": " Deep learning is a subset of machine learning that involves"
|
||||
}
|
||||
]
|
||||
|
|
|
|||
|
|
@ -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]
|
||||
|
|
|
|||
|
|
@ -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"
|
||||
|
|
|
|||
|
|
@ -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 },
|
||||
|
|
|
|||
|
|
@ -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",
|
||||
|
|
|
|||
|
|
@ -1,5 +1,5 @@
|
|||
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
|
||||
|
|
|
|||
|
|
@ -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,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
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:
|
||||
|
|
|
|||
|
|
@ -1,8 +1,38 @@
|
|||
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)
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
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,
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
|
||||
|
|
|
|||
|
|
@ -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(
|
||||
|
|
|
|||
|
|
@ -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))
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
|
|
|||
|
|
@ -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(
|
||||
|
|
|
|||
|
|
@ -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))
|
||||
|
|
|
|||
|
|
@ -36,6 +36,7 @@ from text_generation_server.layers import (
|
|||
SpeculativeHead,
|
||||
get_linear,
|
||||
)
|
||||
from text_generation_server.layers.attention.kv_cache import get_kv_scales
|
||||
|
||||
|
||||
def load_qkv(config, prefix: str, weights, head_size, num_heads):
|
||||
|
|
@ -193,6 +194,7 @@ class FlashGPT2Attention(torch.nn.Module):
|
|||
head_size=self.head_size,
|
||||
num_heads=self.num_heads,
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
|
||||
self.o_proj = load_row(
|
||||
config,
|
||||
|
|
@ -222,7 +224,12 @@ class FlashGPT2Attention(torch.nn.Module):
|
|||
key = key.view(-1, self.num_heads, self.head_size)
|
||||
value = value.view(-1, self.num_heads, self.head_size)
|
||||
|
||||
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:
|
||||
|
|
@ -232,6 +239,7 @@ class FlashGPT2Attention(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,
|
||||
|
|
@ -246,6 +254,7 @@ class FlashGPT2Attention(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))
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ import torch.distributed
|
|||
from torch import nn
|
||||
from transformers.activations import ACT2FN
|
||||
from typing import Optional, List, Tuple
|
||||
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.attention import (
|
||||
paged_attention,
|
||||
|
|
@ -138,6 +139,7 @@ class FlashGPTJAttention(torch.nn.Module):
|
|||
prefix=prefix,
|
||||
weights=weights,
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
|
||||
self.o_proj = load_row(
|
||||
config,
|
||||
|
|
@ -184,7 +186,12 @@ class FlashGPTJAttention(torch.nn.Module):
|
|||
else:
|
||||
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:
|
||||
|
|
@ -194,6 +201,7 @@ class FlashGPTJAttention(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,
|
||||
|
|
@ -208,6 +216,7 @@ class FlashGPTJAttention(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))
|
||||
|
|
|
|||
|
|
@ -27,7 +27,10 @@ import torch.distributed
|
|||
from torch import nn
|
||||
from transformers.activations import ACT2FN
|
||||
|
||||
from text_generation_server.layers.attention import KVCache
|
||||
from text_generation_server.layers.attention import (
|
||||
KVCache,
|
||||
get_kv_scales,
|
||||
)
|
||||
from text_generation_server.layers.moe import DenseMoELayer, MoELayer, SparseMoELayer
|
||||
from text_generation_server.utils.import_utils import SYSTEM
|
||||
from text_generation_server.layers.attention import (
|
||||
|
|
@ -179,6 +182,8 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
self.query_key_value = load_attention(config, prefix, weights, index)
|
||||
self.index = index
|
||||
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
|
||||
o_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
prefix=f"{prefix}.o_proj",
|
||||
|
|
@ -224,7 +229,12 @@ class FlashLlamaAttention(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:
|
||||
|
|
@ -233,6 +243,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
query=query,
|
||||
key=kv[:, 0],
|
||||
value=kv[:, 1],
|
||||
kv_scales=self.kv_scales,
|
||||
kv_cache=kv_cache,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
|
|
@ -248,6 +259,7 @@ class FlashLlamaAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.o_proj(
|
||||
|
|
|
|||
|
|
@ -26,6 +26,7 @@ from transformers.activations import ACT2FN
|
|||
from transformers.configuration_utils import PretrainedConfig
|
||||
from typing import Optional, List, Tuple
|
||||
|
||||
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.attention import (
|
||||
paged_attention,
|
||||
|
|
@ -158,6 +159,7 @@ class MistralAttention(torch.nn.Module):
|
|||
],
|
||||
process_group=weights.process_group,
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
|
||||
o_proj = TensorParallelRowLinear.load(
|
||||
config,
|
||||
|
|
@ -208,7 +210,12 @@ class MistralAttention(torch.nn.Module):
|
|||
else:
|
||||
kv_to_cache = kv
|
||||
|
||||
kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
|
||||
kv_cache.store(
|
||||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
|
|
@ -218,6 +225,7 @@ class MistralAttention(torch.nn.Module):
|
|||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
kv_cache=kv_cache,
|
||||
kv_scales=self.kv_scales,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
softmax_scale=self.softmax_scale,
|
||||
|
|
@ -233,6 +241,7 @@ class MistralAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.o_proj(
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ from text_generation_server.layers.attention import (
|
|||
attention,
|
||||
paged_attention,
|
||||
)
|
||||
from text_generation_server.layers.attention.kv_cache import 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
|
||||
|
|
@ -213,6 +214,7 @@ class MixtralAttention(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,
|
||||
|
|
@ -256,7 +258,12 @@ class MixtralAttention(torch.nn.Module):
|
|||
else:
|
||||
kv_to_cache = kv
|
||||
|
||||
kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
|
||||
kv_cache.store(
|
||||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
|
|
@ -266,6 +273,7 @@ class MixtralAttention(torch.nn.Module):
|
|||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
kv_cache=kv_cache,
|
||||
kv_scales=self.kv_scales,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
softmax_scale=self.softmax_scale,
|
||||
|
|
@ -281,6 +289,7 @@ class MixtralAttention(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))
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ 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.layernorm import (
|
||||
FastLayerNorm,
|
||||
)
|
||||
|
|
@ -130,6 +131,7 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||
head_size=self.head_size,
|
||||
hidden_size=self.hidden_size,
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
self.dense = load_row(
|
||||
config, prefix=f"{prefix}.dense", weights=weights, bias=True
|
||||
)
|
||||
|
|
@ -163,7 +165,12 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||
qkv[:, 0] = torch.cat((query_rot, query_pass), dim=-1)
|
||||
qkv[:, 1] = torch.cat((key_rot, key_pass), dim=-1)
|
||||
|
||||
kv_cache.store(key=qkv[:, 1], value=qkv[:, 2], slots=slots)
|
||||
kv_cache.store(
|
||||
key=qkv[:, 1],
|
||||
value=qkv[:, 2],
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
|
|
@ -173,6 +180,7 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||
key=qkv[:, 1],
|
||||
value=qkv[:, 2],
|
||||
kv_cache=kv_cache,
|
||||
kv_scales=self.kv_scales,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
softmax_scale=self.softmax_scale,
|
||||
|
|
@ -187,6 +195,7 @@ class FlashNeoxAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
|
|
|||
|
|
@ -18,6 +18,7 @@ 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.layernorm import (
|
||||
FastLayerNorm,
|
||||
)
|
||||
|
|
@ -137,6 +138,7 @@ class FlashPhiAttention(torch.nn.Module):
|
|||
)
|
||||
|
||||
self.query_key_value = load_attention(config, prefix, weights)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
|
||||
# in llama the dense layer is called "o_proj" and has bias=False
|
||||
self.dense = TensorParallelRowLinear.load(
|
||||
|
|
@ -186,7 +188,12 @@ class FlashPhiAttention(torch.nn.Module):
|
|||
)
|
||||
|
||||
# Reshape key and value and cache
|
||||
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:
|
||||
|
|
@ -194,6 +201,7 @@ class FlashPhiAttention(torch.nn.Module):
|
|||
query=query,
|
||||
key=kv[:, 0],
|
||||
value=kv[:, 1],
|
||||
kv_scales=self.kv_scales,
|
||||
kv_cache=kv_cache,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
|
|
@ -209,6 +217,7 @@ class FlashPhiAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
|
|
|||
|
|
@ -16,6 +16,7 @@ from text_generation_server.layers import (
|
|||
TensorParallelEmbedding,
|
||||
SpeculativeHead,
|
||||
)
|
||||
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,
|
||||
|
|
@ -84,6 +85,8 @@ class Qwen2Attention(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,
|
||||
prefix=f"{prefix}.o_proj",
|
||||
|
|
@ -126,7 +129,12 @@ class Qwen2Attention(torch.nn.Module):
|
|||
else:
|
||||
kv_to_cache = kv
|
||||
|
||||
kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
|
||||
kv_cache.store(
|
||||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
|
|
@ -136,6 +144,7 @@ class Qwen2Attention(torch.nn.Module):
|
|||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
kv_cache=kv_cache,
|
||||
kv_scales=self.kv_scales,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
softmax_scale=self.softmax_scale,
|
||||
|
|
@ -151,6 +160,7 @@ class Qwen2Attention(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))
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ from text_generation_server.layers import (
|
|||
TensorParallelRowLinear,
|
||||
get_linear,
|
||||
)
|
||||
from text_generation_server.layers.attention.kv_cache import get_kv_scales
|
||||
from text_generation_server.layers.layernorm import FastLayerNorm
|
||||
from text_generation_server.layers.rotary import PositionRotaryEmbedding
|
||||
from text_generation_server.layers.attention import (
|
||||
|
|
@ -158,6 +159,7 @@ class FlashRWAttention(torch.nn.Module):
|
|||
weights=weights,
|
||||
bias=config.bias,
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
self.dense = load_row(
|
||||
config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
|
||||
)
|
||||
|
|
@ -198,7 +200,12 @@ class FlashRWAttention(torch.nn.Module):
|
|||
# Inplace rotary
|
||||
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:
|
||||
|
|
@ -208,6 +215,7 @@ class FlashRWAttention(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,
|
||||
|
|
@ -222,6 +230,7 @@ class FlashRWAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.dense(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
|
@ -276,6 +285,7 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||
weights=weights,
|
||||
bias=config.bias,
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
self.dense = load_row(
|
||||
config, prefix=f"{prefix}.dense", weights=weights, bias=config.bias
|
||||
)
|
||||
|
|
@ -311,7 +321,10 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||
self.rotary_emb(query, torch.select(kv, dim=2, index=0), cos, sin)
|
||||
|
||||
kv_cache.store(
|
||||
key=kv[:, :, 0].contiguous(), value=kv[:, :, 1].contiguous(), slots=slots
|
||||
key=kv[:, :, 0].contiguous(),
|
||||
value=kv[:, :, 1].contiguous(),
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
|
|
@ -322,6 +335,7 @@ class FlashRWLargeAttention(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,
|
||||
|
|
@ -336,6 +350,7 @@ class FlashRWLargeAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.dense(
|
||||
|
|
|
|||
|
|
@ -17,6 +17,7 @@ from text_generation_server.layers import (
|
|||
TensorParallelEmbedding,
|
||||
get_linear,
|
||||
)
|
||||
from text_generation_server.layers.attention.kv_cache import get_kv_scales
|
||||
from text_generation_server.layers.gptq import GPTQWeightsLoader
|
||||
from text_generation_server.layers.layernorm import (
|
||||
FastLayerNorm,
|
||||
|
|
@ -257,6 +258,7 @@ class FlashMQAttention(torch.nn.Module):
|
|||
self.c_proj = load_row(
|
||||
config, prefix=f"{prefix}.c_proj", weights=weights, bias=True
|
||||
)
|
||||
self.kv_scales = get_kv_scales(weights, f"{prefix}")
|
||||
self.kv_head_mapping = torch.zeros(
|
||||
self.num_heads, dtype=torch.int32, device=weights.device
|
||||
)
|
||||
|
|
@ -282,7 +284,12 @@ class FlashMQAttention(torch.nn.Module):
|
|||
query = query.view(-1, self.num_heads, self.head_size)
|
||||
key_value = key_value.view(-1, 2, 1, self.head_size)
|
||||
|
||||
kv_cache.store(key=key_value[:, 0], value=key_value[:, 1], slots=slots)
|
||||
kv_cache.store(
|
||||
key=key_value[:, 0],
|
||||
value=key_value[:, 1],
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
|
|
@ -292,6 +299,7 @@ class FlashMQAttention(torch.nn.Module):
|
|||
key=key_value[:, 0],
|
||||
value=key_value[:, 1],
|
||||
kv_cache=kv_cache,
|
||||
kv_scales=self.kv_scales,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
softmax_scale=self.softmax_scale,
|
||||
|
|
@ -306,6 +314,7 @@ class FlashMQAttention(torch.nn.Module):
|
|||
block_tables,
|
||||
seqlen,
|
||||
max_s,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
return self.c_proj(attn_output.view(-1, self.num_heads * self.head_size))
|
||||
|
|
|
|||
|
|
@ -38,6 +38,7 @@ 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.layernorm import (
|
||||
FastLayerNorm,
|
||||
FastRMSNorm,
|
||||
|
|
@ -188,6 +189,7 @@ class Starcoder2Attention(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,
|
||||
|
|
@ -231,7 +233,12 @@ class Starcoder2Attention(torch.nn.Module):
|
|||
else:
|
||||
kv_to_cache = kv
|
||||
|
||||
kv_cache.store(key=kv_to_cache[:, 0], value=kv_to_cache[:, 1], slots=slots)
|
||||
kv_cache.store(
|
||||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
slots=slots,
|
||||
kv_scales=self.kv_scales,
|
||||
)
|
||||
|
||||
# Prefill
|
||||
if cu_seqlen_prefill is not None:
|
||||
|
|
@ -241,6 +248,7 @@ class Starcoder2Attention(torch.nn.Module):
|
|||
key=kv_to_cache[:, 0],
|
||||
value=kv_to_cache[:, 1],
|
||||
kv_cache=kv_cache,
|
||||
kv_scales=self.kv_scales,
|
||||
seqlen=seqlen,
|
||||
block_tables=block_tables,
|
||||
softmax_scale=self.softmax_scale,
|
||||
|
|
@ -256,6 +264,7 @@ class Starcoder2Attention(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))
|
||||
|
|
|
|||
|
|
@ -2283,6 +2283,7 @@ class FlashCausalLM(Model):
|
|||
num_kv_heads=self.num_kv_heads,
|
||||
head_size=self.head_size,
|
||||
page_size=BLOCK_SIZE,
|
||||
kv_cache_dtype=self.kv_cache_dtype,
|
||||
dtype=self.dtype,
|
||||
window_left=self.sliding_window,
|
||||
)
|
||||
|
|
|
|||
|
|
@ -207,7 +207,9 @@ class Weights:
|
|||
def get_shape(self, tensor_name: str):
|
||||
return self._get_slice(tensor_name).get_shape()
|
||||
|
||||
def get_tensor(self, tensor_name: str, to_device=True, to_dtype=True):
|
||||
def get_tensor(
|
||||
self, tensor_name: str, to_device: bool = True, to_dtype: bool = True
|
||||
) -> torch.Tensor:
|
||||
filename, tensor_name = self.get_filename(tensor_name)
|
||||
f = self._get_handle(filename)
|
||||
tensor = f.get_tensor(tensor_name)
|
||||
|
|
|
|||
Loading…
Reference in New Issue