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Commit ca796e19 authored by zhuwenwen's avatar zhuwenwen
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Merge tag 'v0.8.1' into v0.8.1-ori

parents e983c804 61c7a1b8
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Showing with 1894 additions and 108 deletions
tests/v1/engine/test_async_llm.py
View file @ ca796e19
......@@ -76,21 +76,18 @@ async def generate(engine: AsyncLLM,
@pytest.mark.parametrize(
"output_kind", [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
@pytest.mark.parametrize("engine_args_and_prompt",
@pytest.mark.parametrize("engine_args,prompt",
[(TEXT_ENGINE_ARGS, TEXT_PROMPT),
(VISION_ENGINE_ARGS, VISION_PROMPT)])
@pytest.mark.asyncio
async def test_load(
monkeypatch: pytest.MonkeyPatch,
output_kind: RequestOutputKind,
engine_args_and_prompt: tuple[AsyncEngineArgs, PromptType],
):
async def test_load(monkeypatch: pytest.MonkeyPatch,
output_kind: RequestOutputKind,
engine_args: AsyncEngineArgs, prompt: PromptType):
# TODO(rickyx): Remove monkeypatch once we have a better way to test V1
# so that in the future when we switch, we don't have to change all the
# tests.
with monkeypatch.context() as m, ExitStack() as after:
m.setenv("VLLM_USE_V1", "1")
engine_args, prompt = engine_args_and_prompt
engine = AsyncLLM.from_engine_args(engine_args)
after.callback(engine.shutdown)
......@@ -124,18 +121,16 @@ async def test_load(
@pytest.mark.parametrize(
"output_kind", [RequestOutputKind.DELTA, RequestOutputKind.FINAL_ONLY])
@pytest.mark.parametrize("engine_args_and_prompt",
@pytest.mark.parametrize("engine_args,prompt",
[(TEXT_ENGINE_ARGS, TEXT_PROMPT),
(VISION_ENGINE_ARGS, VISION_PROMPT)])
@pytest.mark.asyncio
async def test_abort(monkeypatch: pytest.MonkeyPatch,
output_kind: RequestOutputKind,
engine_args_and_prompt: tuple[AsyncEngineArgs,
PromptType]):
engine_args: AsyncEngineArgs, prompt: PromptType):
with monkeypatch.context() as m, ExitStack() as after:
m.setenv("VLLM_USE_V1", "1")
engine_args, prompt = engine_args_and_prompt
engine = AsyncLLM.from_engine_args(engine_args)
after.callback(engine.shutdown)
......@@ -193,17 +188,15 @@ async def test_abort(monkeypatch: pytest.MonkeyPatch,
@pytest.mark.parametrize("n", [1, 3])
@pytest.mark.parametrize("engine_args_and_prompt",
@pytest.mark.parametrize("engine_args,prompt",
[(TEXT_ENGINE_ARGS, TEXT_PROMPT),
(VISION_ENGINE_ARGS, VISION_PROMPT)])
@pytest.mark.asyncio
async def test_finished_flag(monkeypatch, n: int,
engine_args_and_prompt: tuple[AsyncEngineArgs,
PromptType]):
async def test_finished_flag(monkeypatch: pytest.MonkeyPatch, n: int,
engine_args: AsyncEngineArgs, prompt: PromptType):
with monkeypatch.context() as m, ExitStack() as after:
m.setenv("VLLM_USE_V1", "1")
engine_args, prompt = engine_args_and_prompt
engine = AsyncLLM.from_engine_args(engine_args)
after.callback(engine.shutdown)
......
tests/v1/engine/test_llm_engine.py
View file @ ca796e19
......@@ -50,7 +50,7 @@ def _get_test_sampling_params(
"""Generate random sampling params for a batch."""
def get_mostly_n_gt1() -> int:
"""Mostly n \in [2,20], ~1/3 n=1"""
r"""Mostly n \in [2,20], ~1/3 n=1"""
x = random.randint(0, 28)
if x < 10:
return 1
......
tests/v1/sample/test_rejection_sampler.py
View file @ ca796e19
......@@ -6,20 +6,23 @@ import torch
import torch.nn.functional as F
from vllm.v1.sample.metadata import SamplingMetadata
from vllm.v1.sample.rejection_sampler import INVALID_TOKEN_ID, RejectionSampler
from vllm.v1.sample.rejection_sampler import (PLACEHOLDER_TOKEN_ID,
RejectionSampler)
from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
DEVICE = "cpu"
DEVICE = "cuda"
@pytest.fixture
def sampler():
def rejection_sampler():
return RejectionSampler()
def create_logits_tensor(token_ids: list[list[int]],
def create_logits_tensor(output_token_ids: list[list[int]],
vocab_size: int = 100) -> torch.Tensor:
"""Helper function to create logits tensor that
will produce desired token ids on argmax"""
token_ids = [tokens[:-1] for tokens in output_token_ids]
num_total_tokens = sum(len(tokens) for tokens in token_ids)
logits = torch.full((num_total_tokens, vocab_size), -100.0, device=DEVICE)
start_loc = 0
......@@ -31,15 +34,22 @@ def create_logits_tensor(token_ids: list[list[int]],
def create_sampling_metadata(
all_greedy: bool,
generators: Optional[dict[int, Any]] = None) -> SamplingMetadata:
all_greedy: bool,
temperature: Optional[torch.Tensor] = None,
generators: Optional[dict[int, Any]] = None,
) -> SamplingMetadata:
"""Create a v1 sampling metadata object with all_greedy set
to the given value. Either all greedy or all random sampling
is used.
"""
generators = generators or {}
if all_greedy:
temperature = None
else:
assert temperature is not None
return SamplingMetadata(
temperature=torch.tensor([]),
temperature=temperature,
all_greedy=all_greedy,
all_random=not all_greedy,
top_p=None,
......@@ -61,7 +71,7 @@ def create_sampling_metadata(
########################### Tests for Greedy Sampling ###################
def test_perfect_match(sampler):
def test_perfect_match(rejection_sampler):
"""Test when output tokens perfectly match speculated tokens"""
spec_tokens = [[1, 2, 3]]
output_tokens = [[1, 2, 3, 4]] # 4 is the bonus token
......@@ -70,15 +80,23 @@ def test_perfect_match(sampler):
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected = torch.tensor([[1, 2, 3, 4]],
dtype=torch.int,
device=logits.device)
assert torch.equal(output, expected)
def test_early_mismatch(sampler):
def test_early_mismatch(rejection_sampler):
"""Test when there's an early mismatch in tokens"""
spec_tokens = [[1, 2, 3]]
output_tokens = [[1, 5, 3, 4]] # Mismatch at position 1
......@@ -87,15 +105,25 @@ def test_early_mismatch(sampler):
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
expected = torch.tensor([[1, 5, INVALID_TOKEN_ID, INVALID_TOKEN_ID]],
dtype=torch.int,
device=logits.device)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected = torch.tensor(
[[1, 5, PLACEHOLDER_TOKEN_ID, PLACEHOLDER_TOKEN_ID]],
dtype=torch.int,
device=logits.device,
)
assert torch.equal(output, expected)
def test_multiple_sequences(sampler):
def test_multiple_sequences(rejection_sampler):
"""Test handling multiple sequences of speculated tokens"""
spec_tokens = [[1, 2], [3]]
output_tokens = [[1, 2, 5], [3,
......@@ -105,15 +133,23 @@ def test_multiple_sequences(sampler):
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor(
[output_tokens[0][-1], output_tokens[1][-1]], device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
expected = torch.tensor([[1, 2, 5], [3, 4, INVALID_TOKEN_ID]],
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected = torch.tensor([[1, 2, 5], [3, 4, PLACEHOLDER_TOKEN_ID]],
dtype=torch.int,
device=logits.device)
assert torch.equal(output, expected)
def test_single_token_sequence(sampler):
def test_single_token_sequence(rejection_sampler):
"""Test handling sequences with single token"""
spec_tokens = [[1]]
output_tokens = [[1, 2]] # Single token with bonus token 2
......@@ -122,13 +158,21 @@ def test_single_token_sequence(sampler):
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected = torch.tensor([[1, 2]], dtype=torch.int, device=logits.device)
assert torch.equal(output, expected)
def test_empty_sequence(sampler):
def test_empty_sequence(rejection_sampler):
"""Test handling empty sequence of speculated tokens"""
spec_tokens: list[list[int]] = [[]]
output_tokens = [[5]] # Just the bonus token
......@@ -137,13 +181,21 @@ def test_empty_sequence(sampler):
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor([output_tokens[0][-1]],
device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected = torch.tensor([[5]], dtype=torch.int, device=logits.device)
assert torch.equal(output, expected)
def test_multiple_mismatches(sampler):
def test_multiple_mismatches(rejection_sampler):
"""Test handling multiple sequences with mismatches"""
spec_tokens = [[1, 2, 3], [4, 5, 6]]
output_tokens = [[1, 2, 7, 6], [4, 8, 6,
......@@ -153,12 +205,22 @@ def test_multiple_mismatches(sampler):
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor(
[output_tokens[0][-1], output_tokens[1][-1]], device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
expected = torch.tensor([[1, 2, 7, INVALID_TOKEN_ID],
[4, 8, INVALID_TOKEN_ID, INVALID_TOKEN_ID]],
dtype=torch.int,
device=logits.device)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected = torch.tensor(
[[1, 2, 7, PLACEHOLDER_TOKEN_ID],
[4, 8, PLACEHOLDER_TOKEN_ID, PLACEHOLDER_TOKEN_ID]],
dtype=torch.int,
device=logits.device,
)
assert torch.equal(output, expected)
......@@ -166,18 +228,27 @@ def test_multiple_mismatches(sampler):
"spec_tokens,output_tokens,expected",
[
([[1, 2]], [[1, 2, 3]], [[1, 2, 3]]), # Perfect match with bonus
([[1]], [[2, 3]], [[2, INVALID_TOKEN_ID]]), # First mismatch
([[1]], [[2, 3]], [[2, PLACEHOLDER_TOKEN_ID]]), # First mismatch
([[1, 2], [3, 4]], [[1, 5, 6], [3, 4, 7]],
[[1, 5, INVALID_TOKEN_ID], [3, 4, 7]]), # Mixed matches
[[1, 5, PLACEHOLDER_TOKEN_ID], [3, 4, 7]]), # Mixed matches
])
def test_parametrized_cases(sampler, spec_tokens, output_tokens, expected):
def test_parametrized_cases(rejection_sampler, spec_tokens, output_tokens,
expected):
"""Parametrized test for various matching scenarios"""
metadata = create_sampling_metadata(all_greedy=True)
logits = create_logits_tensor(output_tokens)
bonus_token_tensor = torch.tensor([tokens[-1] for tokens in output_tokens],
device=logits.device)
output = sampler(spec_tokens, None, bonus_token_tensor, logits, metadata)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(spec_tokens,
device=logits.device)
output = rejection_sampler(
spec_decode_metadata,
draft_probs=None,
target_logits=logits,
bonus_token_ids=bonus_token_tensor,
sampling_metadata=metadata,
)
expected_tensor = torch.tensor(expected,
dtype=torch.int,
device=logits.device)
......@@ -190,21 +261,31 @@ def test_parametrized_cases(sampler, spec_tokens, output_tokens, expected):
@pytest.mark.parametrize("batch_size", [1, 4, 8])
@pytest.mark.parametrize("frac_seeded", [0.0, 0.5])
@pytest.mark.parametrize("n_rep", [20])
def test_deterministic_when_seeded(sampler, k: int, vocab_size: int,
batch_size: int, frac_seeded: float,
n_rep: int):
draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
target_probs = torch.rand(batch_size * (k + 1),
vocab_size,
dtype=torch.float32)
def test_deterministic_when_seeded(
rejection_sampler,
k: int,
vocab_size: int,
batch_size: int,
frac_seeded: float,
n_rep: int,
):
num_tokens = batch_size * k
draft_probs = torch.rand(num_tokens,
vocab_size,
dtype=torch.float32,
device=DEVICE)
draft_probs = F.softmax(draft_probs, dim=-1)
target_logits = torch.rand_like(draft_probs)
bonus_token_ids = torch.randint(low=0,
high=vocab_size,
size=(batch_size, 1),
dtype=torch.int64)
dtype=torch.int64,
device=DEVICE)
draft_token_ids = torch.randint(low=0,
high=vocab_size,
size=(batch_size, k),
dtype=torch.int64)
dtype=torch.int64,
device=DEVICE)
seeded_mask = torch.rand(batch_size, dtype=torch.float32) <= frac_seeded
......@@ -215,10 +296,21 @@ def test_deterministic_when_seeded(sampler, k: int, vocab_size: int,
for i in range(batch_size) if seeded_mask[i]
}
temperature = torch.ones(batch_size,
dtype=torch.float32,
device=DEVICE)
sampling_metadata = create_sampling_metadata(all_greedy=False,
temperature=temperature,
generators=seeded_seqs)
rep_result = sampler(draft_token_ids.tolist(), draft_probs,
bonus_token_ids, target_probs, sampling_metadata)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(
draft_token_ids.tolist(), device=DEVICE)
rep_result = rejection_sampler(
spec_decode_metadata,
draft_probs=draft_probs,
target_logits=target_logits,
bonus_token_ids=bonus_token_ids,
sampling_metadata=sampling_metadata,
)
results.append(rep_result)
......@@ -257,10 +349,10 @@ def test_rejection_sampling_approximates_target_distribution():
num_reference_probs = 100
# Prepare draft, target, and reference probability distributions
draft_probs, target_probs = (F.softmax(
torch.rand(vocab_size, dtype=torch.float32),
dim=-1,
) for _ in range(2))
draft_probs = F.softmax(torch.rand(vocab_size, dtype=torch.float32),
dim=-1)
target_logits = torch.rand(vocab_size, dtype=torch.float32)
target_probs = F.softmax(target_logits, dim=-1)
reference_probs = F.softmax(
torch.rand(num_reference_probs, vocab_size, dtype=torch.float32),
dim=-1,
......@@ -273,7 +365,7 @@ def test_rejection_sampling_approximates_target_distribution():
for num_samples in sample_sizes:
# Sample using rejection sampling.
rej_sample_probs = estimate_rejection_sampling_pdf(
draft_probs, target_probs, k, vocab_size, num_samples)
draft_probs, target_logits, k, vocab_size, num_samples)
rej_sample_probs = rej_sample_probs.to(DEVICE)
# Average distance from reference probs.
......@@ -313,7 +405,7 @@ def get_ratio_first_to_last(elements: list[float]) -> float:
def estimate_rejection_sampling_pdf(
draft_probs: torch.Tensor,
target_probs: torch.Tensor,
target_logits: torch.Tensor,
k: int,
vocab_size: int,
num_samples: int,
......@@ -323,35 +415,44 @@ def estimate_rejection_sampling_pdf(
Args:
draft_probs: Draft probability distribution.
target_probs: Target probability distribution.
target_logits: Target logits.
num_samples: Number of samples to draw.
Returns:
Estimated probability distribution of the output tokens.
"""
sampler = RejectionSampler()
# Repeat draft probs num_samples times.
rejection_sampler = RejectionSampler()
num_tokens = num_samples * k
# Repeat draft probs num_samples * k times.
draft_probs = draft_probs.reshape(1, 1,
vocab_size).repeat(num_samples, k, 1)
# Repeat target probs num_samples * (k + 1) times.
target_probs = target_probs.reshape(1, 1, vocab_size).repeat(
num_samples, k + 1, 1).reshape(num_samples * (k + 1), vocab_size)
# Repeat target probs num_tokens times.
target_logits = target_logits.reshape(1, vocab_size).repeat(num_tokens, 1)
# Randomly sample draft token ids from draft probs.
draft_token_ids = torch.multinomial(draft_probs[:, 0, :],
num_samples=k,
replacement=True).reshape(
num_samples, k)
draft_probs = draft_probs.view(num_tokens, vocab_size)
# Bonus tokens not used but required.
bonus_token_ids = torch.zeros((1, 1), dtype=torch.int64,
device=DEVICE).repeat(num_samples, 1)
sampling_metadata = create_sampling_metadata(all_greedy=False)
output_token_ids = sampler(draft_token_ids.tolist(), draft_probs,
bonus_token_ids, target_probs,
sampling_metadata)
temperature = torch.ones(num_samples, dtype=torch.float32, device=DEVICE)
sampling_metadata = create_sampling_metadata(all_greedy=False,
temperature=temperature)
spec_decode_metadata = SpecDecodeMetadata.make_dummy(
draft_token_ids.tolist(), device=bonus_token_ids.device)
output_token_ids = rejection_sampler(
spec_decode_metadata,
draft_probs=draft_probs,
target_logits=target_logits,
bonus_token_ids=bonus_token_ids,
sampling_metadata=sampling_metadata,
)
output_token_ids = output_token_ids[:, :-1].flatten()
hist = torch.histogram(output_token_ids.to(dtype=torch.float,
......
tests/v1/tpu/test_basic.py
View file @ ca796e19
......@@ -15,9 +15,10 @@ if TYPE_CHECKING:
from tests.conftest import VllmRunner
MODELS = [
"Qwen/Qwen2.5-1.5B-Instruct",
# TODO: Enable this models with v6e
# "Qwen/Qwen2-7B-Instruct",
"meta-llama/Llama-3.1-8B",
# TODO: Add models here as necessary
# "meta-llama/Llama-3.1-8B",
]
TENSOR_PARALLEL_SIZES = [1]
......
vllm/config.py
View file @ ca796e19
......@@ -347,7 +347,7 @@ class ModelConfig:
self.encoder_config = self._get_encoder_config()
self.hf_image_processor_config = get_hf_image_processor_config(
self.model, revision)
self.dtype = _get_and_verify_dtype(self.hf_text_config, dtype)
self.dtype = _get_and_verify_dtype(self.hf_config, dtype)
self.use_async_output_proc = use_async_output_proc
self.mm_processor_kwargs = mm_processor_kwargs
self.disable_mm_preprocessor_cache = disable_mm_preprocessor_cache
......@@ -2526,6 +2526,14 @@ def _get_and_verify_dtype(
# NOTE: getattr(config, "torch_dtype", torch.float32) is not correct
# because config.torch_dtype can be None.
config_dtype = getattr(config, "torch_dtype", None)
# Fallbacks for multi-modal models if the root config
# does not define torch_dtype
if config_dtype is None and hasattr(config, "text_config"):
config_dtype = getattr(config.text_config, "torch_dtype", None)
if config_dtype is None and hasattr(config, "vision_config"):
config_dtype = getattr(config.vision_config, "torch_dtype", None)
if config_dtype is None:
config_dtype = torch.float32
......@@ -2533,16 +2541,8 @@ def _get_and_verify_dtype(
dtype = dtype.lower()
if dtype == "auto":
if config_dtype == torch.float32:
if config.model_type in ("gemma2", "gemma3", "gemma3_text"):
logger.info(
"For Gemma 2 and 3, we downcast float32 to bfloat16 "
"instead of float16 by default. Please specify `dtype` "
"if you want to use float16.")
torch_dtype = torch.bfloat16
else:
# Following the common practice, we use float16 for float32
# models.
torch_dtype = torch.float16
# Following common practice, we use float16 for float32 models
torch_dtype = torch.float16
else:
torch_dtype = config_dtype
......
vllm/engine/arg_utils.py
View file @ ca796e19
......@@ -1469,8 +1469,12 @@ class EngineArgs:
return False
# Need at least Ampere for now (FA support required).
# Skip this check if we are running on a non-GPU platform,
# or if the device capability is not available
# (e.g. in a Ray actor without GPUs).
from vllm.platforms import current_platform
if (current_platform.is_cuda()
and current_platform.get_device_capability()
and current_platform.get_device_capability().major < 8):
_raise_or_fallback(feature_name="Compute Capability < 8.0",
recommend_to_remove=False)
......@@ -1574,6 +1578,13 @@ class EngineArgs:
_raise_or_fallback(feature_name=name, recommend_to_remove=True)
return False
# No support for device type other than CUDA, AMD (experiemntal) or
# TPU (experimental) so far.
if not (current_platform.is_cuda_alike() or current_platform.is_tpu()):
_raise_or_fallback(
feature_name=f"device type={current_platform.device_type}",
recommend_to_remove=False)
return False
#############################################################
# Experimental Features - allow users to opt in.
......
vllm/entrypoints/openai/protocol.py
View file @ ca796e19
......@@ -548,7 +548,7 @@ class ChatCompletionRequest(OpenAIBaseModel):
if top_logprobs < 0:
raise ValueError("`top_logprobs` must be a positive value.")
if not data.get("logprobs"):
if top_logprobs > 0 and not data.get("logprobs"):
raise ValueError(
"when using `top_logprobs`, `logprobs` must be set to true."
)
......
vllm/envs.py
View file @ ca796e19
......@@ -35,7 +35,6 @@ if TYPE_CHECKING:
VLLM_TRACE_FUNCTION: int = 0
VLLM_ATTENTION_BACKEND: Optional[str] = None
VLLM_USE_FLASHINFER_SAMPLER: Optional[bool] = None
VLLM_USE_FLASHINFER_REJECTION_SAMPLER: bool = False
VLLM_FLASHINFER_FORCE_TENSOR_CORES: bool = False
VLLM_PP_LAYER_PARTITION: Optional[str] = None
VLLM_CPU_KVCACHE_SPACE: int = 0
......
vllm/executor/multiproc_worker_utils.py
View file @ ca796e19
......@@ -16,12 +16,8 @@ import torch
from vllm.config import VllmConfig
from vllm.logger import init_logger
from vllm.triton_utils.importing import HAS_TRITON
from vllm.utils import _check_multiproc_method, get_mp_context, run_method
if HAS_TRITON:
from vllm.triton_utils import maybe_set_triton_cache_manager
logger = init_logger(__name__)
T = TypeVar('T')
......@@ -314,7 +310,3 @@ def set_multiprocessing_worker_envs(parallel_config):
current_parallelism, default_omp_num_threads)
os.environ["OMP_NUM_THREADS"] = str(default_omp_num_threads)
torch.set_num_threads(default_omp_num_threads)
# workaround for https://github.com/vllm-project/vllm/issues/6103
if HAS_TRITON and parallel_config.world_size > 1:
maybe_set_triton_cache_manager()
vllm/lora/models.py
View file @ ca796e19
......@@ -30,6 +30,7 @@ from vllm.lora.utils import (from_layer, from_layer_logits_processor,
is_regex_target_modules,
parse_fine_tuned_lora_name, replace_submodule)
from vllm.model_executor.models import SupportsLoRA, supports_multimodal
from vllm.model_executor.models.interfaces import is_pooling_model
from vllm.model_executor.models.module_mapping import MultiModelKeys
from vllm.model_executor.models.utils import PPMissingLayer, WeightsMapper
from vllm.utils import is_pin_memory_available
......@@ -104,6 +105,9 @@ class LoRAModel(AdapterModel):
"""Get LoRA for a given module by name"""
return self.loras.get(module_name, None)
def check_lora_name(self, lora_name: str) -> bool:
return lora_name in self.loras
# (yard1): TODO see if we can derive target_embedding_padding automatically
@classmethod
def from_lora_tensors(
......@@ -335,6 +339,7 @@ class LoRAModelManager(AdapterModelManager):
# Used for long context lora.
self.scaling_factor_to_offset: Dict[float, int] = {}
super().__init__(model)
self.supported_lora_modules = get_supported_lora_modules(self.model)
assert self.supported_lora_modules, "No supported LoRA modules found in"
f"{self.model.__class__.__name__}."
......@@ -350,6 +355,7 @@ class LoRAModelManager(AdapterModelManager):
# In case the model only supports LoRA for
# text modules (e.g. ChatGLM)
and hasattr(self.model, "get_mm_mapping"))
self.is_pooling_model = is_pooling_model(self.model)
self.packed_modules: Dict[str, List[str]] = {}
self.modules: Dict[str, BaseLayerWithLoRA] = {}
# Dict instead of a Set for compatibility with LRUCache.
......@@ -389,7 +395,7 @@ class LoRAModelManager(AdapterModelManager):
lora_model.id, index)
self.lora_index_to_id[index] = lora_model.id
for module_name, module in self.modules.items():
module_lora = lora_model.get_lora(module_name)
module_lora = self._get_lora_layer_weights(lora_model, module_name)
if module_lora:
module_lora.optimize()
# Bias is not explicitly enabled with the flag enable_lora_bias.
......@@ -626,7 +632,7 @@ class LoRAModelManager(AdapterModelManager):
replaced_module: Set[str] = set()
has_replacement = False
for r in new_module_names:
lora = lora_model.get_lora(r)
lora = self._get_lora_layer_weights(lora_model, r)
replacement_loras.append(lora)
if lora:
has_replacement = True
......@@ -637,12 +643,34 @@ class LoRAModelManager(AdapterModelManager):
if replacement_loras[i]:
continue
replacement_loras[i] = None
# HACK Temporary solution for the pool model.
if self.is_pooling_model and not lora_model.check_lora_name(
module_name):
replaced_module_name = module_name.replace("model.", "")
if lora_model.check_lora_name(module_name):
module_name = replaced_module_name
lora_model.loras[module_name] = PackedLoRALayerWeights.pack(
replacement_loras)
# Remove the modules that have been replaced.
for module in replaced_module:
lora_model.loras.pop(module, None)
def _get_lora_layer_weights(
self, lora_model: LoRAModel,
module_name: str) -> Optional[LoRALayerWeights]:
org_module_name = module_name
if self.is_pooling_model and not lora_model.check_lora_name(
module_name):
# If it's a pool model, and the layer name is not found,
# remove the prefix 'model.' and search again.
module_name = module_name.replace("model.", "")
if lora_model.check_lora_name(module_name):
org_module_name = module_name
logger.info_once(
"For the pool model, successfully loaded the LoRA weights "
"after removing the prefix 'model.'.")
return lora_model.get_lora(org_module_name)
def deactivate_adapter(self, adapter_id: int) -> bool:
return deactivate_adapter(adapter_id, self._active_adapters,
self._deactivate_adapter)
......
vllm/model_executor/layers/fused_moe/configs/E=256,N=1024,device_name=AMD_Instinct_MI325X,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
}
}
vllm/model_executor/layers/fused_moe/configs/E=256,N=1024,device_name=AMD_Instinct_MI325_OAM,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"4": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"24": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 16,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"256": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 32,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"512": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 16,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
}
}
vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=AMD_Instinct_MI325X,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 16,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"24": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 4,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 4,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"512": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
}
}
vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=AMD_Instinct_MI325_OAM,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 16,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"24": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 4,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 4,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"512": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
}
}
vllm/model_executor/layers/fused_moe/configs/E=256,N=512,device_name=AMD_Instinct_MI325_OAM,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 16,
"BLOCK_SIZE_K": 256,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"2": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"4": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"8": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"16": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 1
},
"24": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 1,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"32": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 4,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"48": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"64": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"96": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 4,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"128": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 4,
"num_warps": 1,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"256": {
"BLOCK_SIZE_M": 16,
"BLOCK_SIZE_N": 32,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 1,
"num_warps": 2,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"512": {
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 64,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1024": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"1536": {
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 128,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"2048": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"3072": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
},
"4096": {
"BLOCK_SIZE_M": 128,
"BLOCK_SIZE_N": 128,
"BLOCK_SIZE_K": 64,
"GROUP_SIZE_M": 8,
"num_warps": 8,
"num_stages": 2,
"waves_per_eu": 0,
"matrix_instr_nonkdim": 16,
"kpack": 2
}
}
vllm/model_executor/layers/fused_moe/fused_moe.py
View file @ ca796e19
......@@ -783,8 +783,12 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
use_int8_w8a16=use_int8_w8a16,
**config,
)
else:
config = config.copy()
BLOCK_SIZE_K = config.pop("BLOCK_SIZE_K")
if block_shape is not None:
BLOCK_SIZE_K = min(BLOCK_SIZE_K, min(block_shape[0],
block_shape[1]))
fused_moe_kernel[grid](
A,
B,
......@@ -823,6 +827,7 @@ def invoke_fused_moe_kernel(A: torch.Tensor,
compute_type=compute_type,
use_fp8_w8a8=use_fp8_w8a8,
use_int8_w8a16=use_int8_w8a16,
BLOCK_SIZE_K=BLOCK_SIZE_K,
**config,
)
......
vllm/model_executor/layers/quantization/utils/configs/N=1536,K=1536,device_name=AMD_Instinct_MI325X,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"8": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"16": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"24": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"32": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"48": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"64": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"96": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"128": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"256": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"512": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1024": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1536": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2048": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"3072": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4096": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
}
}
\ No newline at end of file
vllm/model_executor/layers/quantization/utils/configs/N=1536,K=1536,device_name=AMD_Instinct_MI325_OAM,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"8": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"16": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"24": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"32": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"48": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"64": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"96": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"128": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"256": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"512": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1024": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1536": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2048": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"3072": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4096": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
}
}
\ No newline at end of file
vllm/model_executor/layers/quantization/utils/configs/N=1536,K=7168,device_name=AMD_Instinct_MI325X,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"8": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"16": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"24": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"32": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"48": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"64": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"96": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"128": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"256": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"512": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1024": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1536": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2048": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"3072": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4096": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
}
}
\ No newline at end of file
vllm/model_executor/layers/quantization/utils/configs/N=1536,K=7168,device_name=AMD_Instinct_MI325_OAM,dtype=fp8_w8a8,block_shape=[128,128].json 0 → 100644
View file @ ca796e19
{
"1": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"8": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"16": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"24": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"32": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"48": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"64": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"96": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"128": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 16,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"256": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"512": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 32,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1024": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 8,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"1536": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"2048": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 32,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 1,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"3072": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
},
"4096": {
"BLOCK_SIZE_K": 128,
"BLOCK_SIZE_M": 64,
"BLOCK_SIZE_N": 128,
"GROUP_SIZE_M": 32,
"kpack": 1,
"matrix_instr_nonkdim": 16,
"num_warps": 4
}
}
\ No newline at end of file
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