[gpt-oss] Add gpt-oss bf16 support

vllm/model_executor/__init__.py

@@ -0,0 +1,16 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from vllm.model_executor.parameter import (BasevLLMParameter,
+                                           PackedvLLMParameter)
+from vllm.model_executor.sampling_metadata import (SamplingMetadata,
+                                                   SamplingMetadataCache)
+from vllm.model_executor.utils import set_random_seed
+
+__all__ = [
+    "SamplingMetadata",
+    "SamplingMetadataCache",
+    "set_random_seed",
+    "BasevLLMParameter",
+    "PackedvLLMParameter",
+]

vllm/model_executor/custom_op.py

@@ -0,0 +1,152 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import torch.nn as nn
+
+from vllm.config import get_current_vllm_config
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+
+logger = init_logger(__name__)
+
+
+class CustomOp(nn.Module):
+    """
+    Base class for custom ops.
+    Dispatches the forward method to the appropriate backend.
+    """
+
+    def __init__(self):
+        super().__init__()
+        self._forward_method = self.dispatch_forward()
+
+    def forward(self, *args, **kwargs):
+        return self._forward_method(*args, **kwargs)
+
+    def forward_native(self, *args, **kwargs):
+        """PyTorch-native implementation of the forward method.
+        This method is optional. If implemented, it can be used with compilers
+        such as torch.compile or PyTorch XLA. Also, it can be used for testing
+        purposes.
+        """
+        raise NotImplementedError
+
+    def forward_cuda(self, *args, **kwargs):
+        raise NotImplementedError
+
+    def forward_hip(self, *args, **kwargs):
+        # By default, we assume that HIP ops are compatible with CUDA ops.
+        return self.forward_cuda(*args, **kwargs)
+
+    def forward_xpu(self, *args, **kwargs):
+        # By default, we assume that XPU ops are compatible with the
+        # PyTorch-native implementation.
+        return self.forward_native(*args, **kwargs)
+
+    def forward_cpu(self, *args, **kwargs):
+        # By default, we assume that CPU ops are compatible with CUDA ops.
+        return self.forward_cuda(*args, **kwargs)
+
+    def forward_tpu(self, *args, **kwargs):
+        # By default, we assume that TPU ops are compatible with the
+        # PyTorch-native implementation.
+        # NOTE(woosuk): This is a placeholder for future extensions.
+        return self.forward_native(*args, **kwargs)
+
+    def forward_hpu(self, *args, **kwargs):
+        # By default, we assume that Gaudi ops are compatible with the
+        # PyTorch-native implementation.
+        return self.forward_native(*args, **kwargs)
+
+    def forward_neuron(self, *args, **kwargs):
+        # By default, we assume that Neuron ops are compatible with the
+        # PyTorch-native implementation.
+        return self.forward_native(*args, **kwargs)
+
+    def forward_oot(self, *args, **kwargs):
+        # By default, we assume that OOT ops are compatible with the
+        # PyTorch-native implementation.
+        return self.forward_native(*args, **kwargs)
+
+    def dispatch_forward(self):
+        # NOTE(woosuk): Here we assume that vLLM was built for only one
+        # specific backend. Currently, we do not support dynamic dispatching.
+        compilation_config = get_current_vllm_config().compilation_config
+        enabled = self.enabled()
+        if enabled:
+            compilation_config.enabled_custom_ops.update([self.__class__.name])
+        else:
+            compilation_config.disabled_custom_ops.update(
+                [self.__class__.name])
+
+        if not enabled:
+            return self.forward_native
+
+        if current_platform.is_rocm():
+            return self.forward_hip
+        elif current_platform.is_cpu():
+            return self.forward_cpu
+        elif current_platform.is_hpu():
+            return self.forward_hpu
+        elif current_platform.is_tpu():
+            return self.forward_tpu
+        elif current_platform.is_xpu():
+            return self.forward_xpu
+        elif current_platform.is_neuron():
+            return self.forward_neuron
+        elif current_platform.is_out_of_tree():
+            return self.forward_oot
+        else:
+            return self.forward_cuda
+
+    @classmethod
+    def enabled(cls) -> bool:
+        # if no name, then it was not registered
+        compilation_config = get_current_vllm_config().compilation_config
+        custom_ops = compilation_config.custom_ops
+        if not hasattr(cls, "name"):
+            logger.warning_once(
+                "Custom op %s was not registered, which means it won't appear in the op registry. It will be enabled/disabled based on the global settings.",  # noqa: E501
+                cls.__name__,
+            )
+            return CustomOp.default_on()
+
+        enabled = f"+{cls.name}" in custom_ops
+        disabled = f"-{cls.name}" in custom_ops
+        assert not (enabled
+                    and disabled), f"Cannot enable and disable {cls.name}"
+
+        return (CustomOp.default_on() or enabled) and not disabled
+
+    @staticmethod
+    def default_on() -> bool:
+        """
+        On by default if level < CompilationLevel.PIECEWISE
+        Specifying 'all' or 'none' in custom_op takes precedence.
+        """
+        from vllm.config import CompilationLevel
+        compilation_config = get_current_vllm_config().compilation_config
+        custom_ops = compilation_config.custom_ops
+        count_none = custom_ops.count("none")
+        count_all = custom_ops.count("all")
+        return compilation_config.level < CompilationLevel.PIECEWISE and \
+            not count_none > 0 or count_all > 0
+
+    # Dictionary of all custom ops (classes, indexed by registered name).
+    # To check if an op with a name is enabled, call .enabled() on the class.
+    # Examples:
+    # - MyOp.enabled()
+    # - op_registry["my_op"].enabled()
+    op_registry: dict[str, type['CustomOp']] = {}
+
+    # Decorator to register custom ops.
+    @classmethod
+    def register(cls, name: str):
+
+        def decorator(op_cls):
+            assert name not in cls.op_registry, f"Duplicate op name: {name}"
+            op_cls.name = name
+            cls.op_registry[name] = op_cls
+            return op_cls
+
+        return decorator

vllm/model_executor/guided_decoding/__init__.py

@@ -0,0 +1,181 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from vllm.logger import init_logger
+from vllm.model_executor.guided_decoding.utils import (
+    convert_lark_to_gbnf, grammar_is_likely_lark,
+    has_lmf_unsupported_json_features, has_xgrammar_unsupported_json_features)
+from vllm.reasoning import ReasoningParserManager
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
+    from vllm.config import ModelConfig
+    from vllm.logits_process import LogitsProcessor
+    from vllm.sampling_params import GuidedDecodingParams
+
+logger = init_logger(__name__)
+
+
+def maybe_backend_fallback(
+        guided_params: GuidedDecodingParams) -> GuidedDecodingParams:
+
+    def fallback_or_error(guided_params: GuidedDecodingParams, message: str,
+                          fallback: str) -> None:
+        """Change the backend to the specified fallback with a warning log,
+        or raise a ValueError if the `disable_fallback` option is specified."""
+        if guided_params.disable_fallback:
+            raise ValueError(message)
+
+        logger.warning("%s Falling back to use %s instead.", message, fallback)
+        guided_params.backend = fallback
+
+    # `auto` was added for V1 to explicitly declare a mode that has fallbacks
+    # in place. If that is specified with V0, treat it as `xgrammar`, as we have
+    # fallbacks enabled for that and it is the V0 default.
+    if guided_params.backend == "auto":
+        guided_params.backend = "xgrammar"
+
+    # lm-format-enforce doesn't support grammar, fallback to xgrammar
+    if guided_params.backend == "lm-format-enforcer":
+        if guided_params.grammar is not None:
+            fallback_or_error(
+                guided_params,
+                "lm-format-enforcer does not support grammar guided decoding.",
+                "xgrammar")
+
+        # lm-format-enforcer doesn't support some JSON schema features
+        elif (guided_params.json is not None
+              and has_lmf_unsupported_json_features(guided_params.json)):
+            fallback_or_error(
+                guided_params,
+                "lm-format-enforcer does not support advanced JSON schema "
+                "features like patterns or numeric ranges.", "outlines")
+
+    if guided_params.backend == "xgrammar":
+        from vllm.model_executor.guided_decoding.xgrammar_decoding import (
+            xgr_installed)
+
+        # xgrammar doesn't support some JSON schema features
+        if (guided_params.json is not None and
+                has_xgrammar_unsupported_json_features(guided_params.json)):
+            fallback_or_error(
+                guided_params,
+                "xgrammar does not support advanced JSON schema features like "
+                "string length, item limits, or property bounds.", "outlines")
+
+        # xgrammar only supports GBNF grammars, so we must convert Lark.
+        # We must check if the grammar is likely Lark and if that
+        # grammar is convertible to GBNF
+        elif (guided_params.grammar is not None
+              and grammar_is_likely_lark(guided_params.grammar)):
+            try:
+                convert_lark_to_gbnf(guided_params.grammar)
+            except Exception:
+                fallback_or_error(
+                    guided_params,
+                    "xgrammar does not support Lark grammars and the "
+                    "grammar failed to convert to GBNF.", "outlines")
+
+        # If the xgrammar module cannot be imported successfully,
+        # we should still allow users to use guided decoding with a fallback.
+        elif not xgr_installed:
+            fallback_or_error(
+                guided_params,
+                "xgrammar module cannot be imported successfully.", "outlines")
+
+    if (guided_params.backend == "outlines"
+            and guided_params.json_object is not None):
+        # outlines doesn't support json_object, fallback to guidance
+        fallback_or_error(guided_params,
+                          "outlines does not support json_object.", "guidance")
+
+    return guided_params
+
+
+async def get_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer: PreTrainedTokenizer,
+        model_config: ModelConfig,
+        reasoning_backend: str | None = None) -> LogitsProcessor | None:
+
+    reasoner = None
+    if reasoning_backend:
+        reasoner_class = ReasoningParserManager.get_reasoning_parser(
+            reasoning_backend)
+        reasoner = reasoner_class(tokenizer)
+
+    guided_params = maybe_backend_fallback(guided_params)
+
+    # CFG grammar not supported by LMFE, so we use outlines instead
+    if guided_params.backend == 'outlines':
+        # NOTE: lazy import outlines to avoid https://github.com/vllm-project/vllm/issues/4193
+        from vllm.model_executor.guided_decoding.outlines_decoding import (  # noqa
+            get_outlines_guided_decoding_logits_processor)
+        return await get_outlines_guided_decoding_logits_processor(
+            guided_params, tokenizer, reasoner)
+    if guided_params.backend == 'lm-format-enforcer':
+        from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (  # noqa
+            get_local_lm_format_enforcer_guided_decoding_logits_processor)
+        return get_local_lm_format_enforcer_guided_decoding_logits_processor(
+            guided_params, tokenizer)
+    if guided_params.backend == 'xgrammar':
+        from vllm.model_executor.guided_decoding.xgrammar_decoding import (  # noqa
+            get_local_xgrammar_guided_decoding_logits_processor)
+        return get_local_xgrammar_guided_decoding_logits_processor(
+            guided_params, tokenizer, model_config, reasoner)
+    if guided_params.backend == 'guidance':
+        from vllm.model_executor.guided_decoding.guidance_decoding import (
+            get_local_guidance_guided_decoding_logits_processor)
+        return get_local_guidance_guided_decoding_logits_processor(
+            guided_params, tokenizer)
+    raise ValueError(
+        f"Unknown guided decoding backend '{guided_params.backend}'. "
+        "Must be one of 'outlines, 'lm-format-enforcer', 'xgrammar', 'guidance'"
+    )
+
+
+def get_local_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer: PreTrainedTokenizer,
+        model_config: ModelConfig,
+        reasoning_backend: str | None = None) -> LogitsProcessor | None:
+    guided_params = maybe_backend_fallback(guided_params)
+
+    reasoner = None
+    if reasoning_backend:
+        reasoner_class = ReasoningParserManager.get_reasoning_parser(
+            reasoning_backend)
+        reasoner = reasoner_class(tokenizer)
+
+    # CFG grammar not supported by LMFE, so we use outlines instead
+    if guided_params.backend == 'outlines':
+        # NOTE: lazy import outlines to avoid https://github.com/vllm-project/vllm/issues/4193
+        from vllm.model_executor.guided_decoding.outlines_decoding import (  # noqa
+            get_local_outlines_guided_decoding_logits_processor)
+        return get_local_outlines_guided_decoding_logits_processor(
+            guided_params, tokenizer, reasoner)
+    if guided_params.backend == 'lm-format-enforcer':
+        from vllm.model_executor.guided_decoding.lm_format_enforcer_decoding import (  # noqa
+            get_local_lm_format_enforcer_guided_decoding_logits_processor)
+        return get_local_lm_format_enforcer_guided_decoding_logits_processor(
+            guided_params, tokenizer)
+    if guided_params.backend == 'xgrammar':
+        from vllm.model_executor.guided_decoding.xgrammar_decoding import (  # noqa
+            get_local_xgrammar_guided_decoding_logits_processor)
+        return get_local_xgrammar_guided_decoding_logits_processor(
+            guided_params, tokenizer, model_config, reasoner)
+    if guided_params.backend == 'guidance':
+        from vllm.model_executor.guided_decoding.guidance_decoding import (
+            get_local_guidance_guided_decoding_logits_processor)
+        return get_local_guidance_guided_decoding_logits_processor(
+            guided_params, tokenizer)
+
+    raise ValueError(
+        f"Unknown guided decoding backend '{guided_params.backend}'. "
+        "Must be one of 'outlines, 'lm-format-enforcer', 'xgrammar', 'guidance'"
+    )

vllm/model_executor/guided_decoding/guidance_decoding.py

@@ -0,0 +1,63 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import json
+
+import llguidance
+from regex import escape as regex_escape
+from transformers import PreTrainedTokenizerBase
+
+from vllm.model_executor.guided_decoding.guidance_logits_processors import (
+    GuidanceLogitsProcessor)
+from vllm.sampling_params import GuidedDecodingParams
+from vllm.v1.structured_output.backend_guidance import (
+    process_for_additional_properties)
+
+
+def get_local_guidance_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer: PreTrainedTokenizerBase) -> GuidanceLogitsProcessor:
+    """
+    Given an OpenAI-compatible request, check for guided decoding parameters
+    and get the necessary logits processor for the given guide.
+    """
+
+    grm = ""
+    any_whitespace = not guided_params.disable_any_whitespace
+    if (guide_json := guided_params.json) is not None:
+        # Optionally set additionalProperties to False at the top-level
+        # By default, other backends do not allow additional top-level
+        # properties, so this makes guidance more similar to other backends
+        if guided_params.disable_additional_properties:
+            if not isinstance(guide_json, str):
+                guide_json = json.dumps(guide_json)
+            guide_json = process_for_additional_properties(guide_json)
+
+        grm = llguidance.LLMatcher.grammar_from_json_schema(
+            guide_json,
+            overrides={"whitespace_pattern": guided_params.whitespace_pattern},
+            defaults={
+                "whitespace_flexible": any_whitespace,
+            })
+    elif guided_params.json_object:
+        grm = llguidance.LLMatcher.grammar_from_json_schema(
+            '{"type": "object"}',
+            overrides={"whitespace_pattern": guided_params.whitespace_pattern},
+            defaults={
+                "whitespace_flexible": any_whitespace,
+            })
+    elif guided_params.regex:
+        grm = llguidance.grammar_from("regex", guided_params.regex)
+    elif guided_params.choice:
+        # choice just uses regex
+        choices = (regex_escape(str(choice))
+                   for choice in guided_params.choice)
+        choices_regex = "(" + "|".join(choices) + ")"
+        grm = llguidance.grammar_from("regex", choices_regex)
+    elif guided_params.grammar:
+        # this supports Lark and GBNF
+        grm = llguidance.grammar_from("grammar", guided_params.grammar)
+
+    if grm:
+        return GuidanceLogitsProcessor(grm, tokenizer)
+
+    raise ValueError("Unknown guided decoding mode")

vllm/model_executor/guided_decoding/guidance_logits_processors.py

@@ -0,0 +1,104 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+import copy
+import os
+from typing import Any
+
+import llguidance
+import llguidance.hf
+import llguidance.torch
+import torch
+from transformers import PreTrainedTokenizerBase
+
+from vllm.logger import init_logger
+
+logger = init_logger(__name__)
+
+
+class GuidanceLogitsProcessor:
+    """Base Guidance Logits Processor"""
+
+    cached_tokenizers: dict[str, Any] = {}
+
+    def __init__(
+        self,
+        grammar: str,
+        tokenizer: PreTrainedTokenizerBase,
+    ) -> None:
+        """Base Guidance Logits Processor
+
+        Args:
+            grammar (str)
+                grammar to guide the generation
+            tokenizer (PreTrainedTokenizerBase)
+                model's tokenizer
+        """
+        self.grammar = grammar
+        self.tokenizer = tokenizer
+        self.tokenizer_name = tokenizer.name_or_path
+        self.ll_tokenizer = None
+        self.ll_matcher = None
+        self.bitmask = None
+        self.new_sampling = False
+        self.initialized = False
+
+    def clone(self) -> "GuidanceLogitsProcessor":
+        cloned = copy.copy(self)
+        if self.initialized:
+            cloned.ll_matcher = llguidance.LLMatcher(
+                self.ll_tokenizer,  # type: ignore[assignment]
+                self.grammar,
+                log_level=int(os.environ.get("LLGUIDANCE_LOG_LEVEL", "1")),
+            )
+            self.bitmask = llguidance.torch.allocate_token_bitmask(
+                1, self.ll_tokenizer.vocab_size)  # type: ignore[attr-defined]
+        return cloned
+
+    def _initialize(self):
+        if self.initialized:
+            return
+
+        ll_tokenizer = self.cached_tokenizers.get(self.tokenizer.name_or_path,
+                                                  None)
+        if ll_tokenizer is None:
+            ll_tokenizer = llguidance.hf.from_tokenizer(self.tokenizer, None)
+            self.cached_tokenizers[self.tokenizer.name_or_path] = ll_tokenizer
+
+        self.ll_tokenizer = ll_tokenizer
+        self.ll_matcher = llguidance.LLMatcher(
+            self.ll_tokenizer,
+            self.grammar,
+            log_level=int(os.environ.get("LLGUIDANCE_LOG_LEVEL", "1")),
+        )
+
+        # create reusable bitmask
+        self.bitmask = llguidance.torch.allocate_token_bitmask(
+            1, self.ll_tokenizer.vocab_size)  # type: ignore[attr-defined]
+
+        self.initialized = True
+
+    def __call__(
+        self,
+        input_ids: list[int],
+        scores: torch.Tensor,
+    ) -> torch.Tensor:
+        # we initialize the guidance model here
+        # to avoid pickling ll_tokenizer and ll_interpreter
+        self._initialize()
+
+        if self.new_sampling and len(input_ids) > 0:
+            self.ll_matcher.consume_token(  # type: ignore[attr-defined]
+                input_ids[-1])
+            err = self.ll_matcher.get_error()  # type: ignore[attr-defined]
+            if err:
+                logger.warning("Error in LLMatcher: %s", err)
+
+        llguidance.torch.fill_next_token_bitmask(self.ll_matcher, self.bitmask,
+                                                 0)
+        llguidance.torch.apply_token_bitmask_inplace(
+            scores,
+            self.bitmask.to(scores.device))  # type: ignore[attr-defined]
+
+        self.new_sampling = True
+
+        return scores

vllm/model_executor/guided_decoding/guided_fields.py

@@ -0,0 +1,41 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from dataclasses import dataclass
+from typing import Optional, TypedDict, Union
+
+
+# These classes are deprecated, see SamplingParams
+class LLMGuidedOptions(TypedDict, total=False):
+    guided_json: Union[dict, str]
+    guided_regex: str
+    guided_choice: list[str]
+    guided_grammar: str
+    guided_decoding_backend: str
+    guided_whitespace_pattern: str
+    guided_json_object: bool
+
+
+@dataclass
+class GuidedDecodingRequest:
+    """One of the fields will be used to retrieve the logit processor."""
+    guided_json: Optional[Union[dict, str]] = None
+    guided_regex: Optional[str] = None
+    guided_choice: Optional[list[str]] = None
+    guided_grammar: Optional[str] = None
+    guided_decoding_backend: Optional[str] = None
+    guided_whitespace_pattern: Optional[str] = None
+    guided_json_object: Optional[bool] = None
+    structural_tag: Optional[str] = None
+
+    def __post_init__(self):
+        """Validate that some fields are mutually exclusive."""
+        guide_count = sum(x is not None
+                          for x in (self.guided_json, self.guided_regex,
+                                    self.guided_choice, self.guided_grammar,
+                                    self.guided_json_object,
+                                    self.structural_tag))
+        if guide_count > 1:
+            raise ValueError(
+                "You can only use one kind of guided decoding but multiple are "
+                f"specified: {self.__dict__}")

vllm/model_executor/guided_decoding/lm_format_enforcer_decoding.py

@@ -0,0 +1,67 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from functools import lru_cache
+from json import loads as json_loads
+from typing import Optional, Union
+
+from lmformatenforcer import (CharacterLevelParser, JsonSchemaParser,
+                              RegexParser, StringParser,
+                              TokenEnforcerTokenizerData, UnionParser)
+from lmformatenforcer.integrations.vllm import (
+    build_vllm_logits_processor, build_vllm_token_enforcer_tokenizer_data)
+from transformers import PreTrainedTokenizerBase
+
+from vllm.logits_process import LogitsProcessor
+from vllm.sampling_params import GuidedDecodingParams
+
+
+def get_local_lm_format_enforcer_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer) -> Optional[LogitsProcessor]:
+    """
+    Given an OpenAI-compatible request, check for guided decoding parameters
+    and get the necessary logits processor for the given guide.
+    We cache logit processors by (guide, tokenizer), and on cache hit
+    we make a shallow copy to reuse the same underlying FSM.
+    """
+
+    tokenizer_data = _cached_build_vllm_token_enforcer_tokenizer_data(
+        tokenizer)
+    character_level_parser: CharacterLevelParser
+    if guided_params.json:
+        schema_dict = _normalize_json_schema_object(guided_params.json)
+        character_level_parser = JsonSchemaParser(schema_dict)
+    elif guided_params.choice:
+        character_level_parser = UnionParser(
+            [StringParser(choice) for choice in guided_params.choice])
+    elif guided_params.regex:
+        character_level_parser = RegexParser(guided_params.regex)
+    elif guided_params.grammar:
+        # CFG grammar not supported by LMFE
+        raise ValueError("Cannot construct a guided decoding logits processor"
+                         " using the grammar option with the"
+                         " lm_format_enforcer backend.")
+    elif guided_params.json_object:
+        # None means any json object
+        character_level_parser = JsonSchemaParser(None)
+    else:
+        return None
+
+    logits_processor = build_vllm_logits_processor(tokenizer_data,
+                                                   character_level_parser)
+    return logits_processor
+
+
+def _normalize_json_schema_object(schema: Union[str, dict]) -> dict:
+    if isinstance(schema, str):
+        return json_loads(schema)
+    if isinstance(schema, dict):
+        return schema
+    raise AssertionError(f"Unsupported schema type {schema}")
+
+
+@lru_cache
+def _cached_build_vllm_token_enforcer_tokenizer_data(
+        tokenizer: PreTrainedTokenizerBase) -> TokenEnforcerTokenizerData:
+    return build_vllm_token_enforcer_tokenizer_data(tokenizer)

vllm/model_executor/guided_decoding/outlines_decoding.py

@@ -0,0 +1,155 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import asyncio
+import concurrent.futures
+import os
+from enum import Enum
+from json import dumps as json_dumps
+from typing import Optional, Union
+
+from regex import escape as regex_escape
+from transformers import PreTrainedTokenizerBase
+
+from vllm.model_executor.guided_decoding.outlines_logits_processors import (
+    CFGLogitsProcessor, JSONLogitsProcessor, RegexLogitsProcessor)
+from vllm.reasoning import ReasoningParser
+from vllm.sampling_params import GuidedDecodingParams
+
+
+class GuidedDecodingMode(Enum):
+    JSON = "json"
+    REGEX = "regex"
+    CHOICE = "choice"
+    GRAMMAR = "grammar"
+
+
+# https://github.com/outlines-dev/outlines/blob/main/outlines/grammars/json.lark
+# the main difference is that we changed the start: value to
+# start: object | array, so we are denying scalar values as the root of the
+# JSON. Starting with scalars as the root seems to cause llama to generate
+# without stop.
+JSON_GRAMMAR = r"""
+?start: object | array
+
+?value: object
+| array
+| UNESCAPED_STRING
+| SIGNED_NUMBER      -> number
+| "true"             -> true
+| "false"            -> false
+| "null"             -> null
+
+array  : "[" [value ("," value)*] "]"
+object : "{" [pair ("," pair)*] "}"
+pair   : UNESCAPED_STRING ":" value
+
+%import common.UNESCAPED_STRING
+%import common.SIGNED_NUMBER
+%import common.WS
+
+%ignore WS
+"""
+
+global_thread_pool = None  # used for generating logits processor fsm
+
+# It's not yet clear that using more provides a benefit, and it could
+# potentially starve other processes on the machine. We'll cap this for now and
+# adjust later if testing proves it to help overcome a bottleneck.
+_MAX_THREADPOOL_WORKERS = 16
+
+
+async def get_outlines_guided_decoding_logits_processor(
+    guided_params: GuidedDecodingParams,
+    tokenizer: PreTrainedTokenizerBase,
+    reasoner: Optional[ReasoningParser],
+) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor,
+           None]:
+    """
+    Given an OpenAI-compatible request, check for guided decoding parameters
+    and get the necessary logits processor for the given guide.
+    We cache logit processors by (guide, tokenizer), and on cache hit
+    we make a shallow copy to reuse the same underlying FSM.
+    """
+    global global_thread_pool
+    guide, mode = _get_guide_and_mode(guided_params)
+    if not guide or not mode:
+        return None
+
+    if global_thread_pool is None:
+        max_workers = os.cpu_count() or 2
+        if max_workers > _MAX_THREADPOOL_WORKERS:
+            max_workers = _MAX_THREADPOOL_WORKERS
+        global_thread_pool = concurrent.futures.ThreadPoolExecutor(
+            max_workers=max_workers)
+    loop = asyncio.get_running_loop()
+
+    return await loop.run_in_executor(global_thread_pool,
+                                      _get_logits_processor, guide, tokenizer,
+                                      mode, guided_params.whitespace_pattern,
+                                      reasoner)
+
+
+def get_local_outlines_guided_decoding_logits_processor(
+    guided_params: GuidedDecodingParams,
+    tokenizer: PreTrainedTokenizerBase,
+    reasoner: Optional[ReasoningParser],
+) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor,
+           None]:
+    """
+    Given an OpenAI-compatible request, check for guided decoding parameters
+    and get the necessary logits processor for the given guide.
+    We cache logit processors by (guide, tokenizer), and on cache hit
+    we make a shallow copy to reuse the same underlying FSM.
+    """
+    guide, mode = _get_guide_and_mode(guided_params)
+    if not guide or not mode:
+        return None
+
+    return _get_logits_processor(guide, tokenizer, mode,
+                                 guided_params.whitespace_pattern, reasoner)
+
+
+def _get_guide_and_mode(
+    guided_params: GuidedDecodingParams
+) -> Union[tuple[str, GuidedDecodingMode], tuple[None, None]]:
+    if guided_params.json:
+        if isinstance(guided_params.json, dict):
+            # turn dict into hashable string
+            json = json_dumps(guided_params.json)
+        else:
+            json = guided_params.json
+        return json, GuidedDecodingMode.JSON
+    elif guided_params.regex:
+        return guided_params.regex, GuidedDecodingMode.REGEX
+    elif guided_params.choice:
+        # choice just uses regex
+        choices = [
+            regex_escape(str(choice)) for choice in guided_params.choice
+        ]
+        choices_regex = "(" + "|".join(choices) + ")"
+        return choices_regex, GuidedDecodingMode.CHOICE
+    elif guided_params.grammar:
+        return guided_params.grammar, GuidedDecodingMode.GRAMMAR
+    elif guided_params.json_object:
+        return JSON_GRAMMAR, GuidedDecodingMode.GRAMMAR
+    else:
+        return None, None
+
+
+def _get_logits_processor(
+    guide: str,
+    tokenizer: PreTrainedTokenizerBase,
+    mode: GuidedDecodingMode,
+    whitespace_pattern: Union[str, None],
+    reasoner: Optional[ReasoningParser],
+) -> Union[JSONLogitsProcessor, RegexLogitsProcessor, CFGLogitsProcessor]:
+    if mode == GuidedDecodingMode.JSON:
+        return JSONLogitsProcessor(guide, tokenizer, whitespace_pattern,
+                                   reasoner)
+    elif mode == GuidedDecodingMode.REGEX or mode == GuidedDecodingMode.CHOICE:
+        return RegexLogitsProcessor(guide, tokenizer, reasoner)
+    elif mode == GuidedDecodingMode.GRAMMAR:
+        return CFGLogitsProcessor(guide, tokenizer, reasoner)
+    else:
+        raise ValueError(f"Unknown guided decoding mode {mode}")

vllm/model_executor/guided_decoding/outlines_logits_processors.py

@@ -0,0 +1,284 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# Copyright 2024- the Outlines developers
+# This file is adapted from
+# https://github.com/outlines-dev/outlines/blob/main/outlines/serve/vllm.py
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+
+#     http://www.apache.org/licenses/LICENSE-2.0
+
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+import copy
+import json
+from collections import defaultdict
+from functools import lru_cache
+from typing import Callable, Optional, Union
+
+import numpy as np
+import torch
+from outlines import grammars
+from outlines.caching import cache, disable_cache
+from outlines.fsm.guide import (CFGGuide, CFGState, Generate, Guide,
+                                RegexGuide, Write)
+from outlines.fsm.parsing import PartialLark
+from outlines_core.fsm.json_schema import build_regex_from_schema
+from pydantic import BaseModel
+from transformers import PreTrainedTokenizerBase
+
+import vllm.envs as envs
+from vllm.logger import init_logger
+from vllm.platforms import current_platform
+from vllm.reasoning import ReasoningParser
+
+logger = init_logger(__name__)
+
+if envs.VLLM_V0_USE_OUTLINES_CACHE:
+    logger.warning("Enabling outlines cache. This is an unbounded on-disk "
+                   "cache. It may consume a lot of disk space and should "
+                   "not be used with untrusted clients.")
+else:
+    disable_cache()
+
+
+class BaseLogitsProcessor:
+
+    def __init__(self, guide: Guide, reasoner: Optional[ReasoningParser]):
+        self._guide: Guide = guide
+        self._reasoner: Optional[ReasoningParser] = reasoner
+        # CFGState is used for the FSM state for CFGGuide
+        self._fsm_state: defaultdict[int, Union[int,
+                                                CFGState]] = defaultdict(int)
+
+    def clone(self) -> "BaseLogitsProcessor":
+        cloned = copy.copy(self)
+        cloned._guide = self._guide.copy()
+        cloned._fsm_state = copy.deepcopy(self._fsm_state)
+        return cloned
+
+    def __call__(self, input_ids: list[int],
+                 scores: torch.Tensor) -> torch.Tensor:
+        """Use the FSM to bias the logits before sampling the next token."""
+
+        # Skip the structured logits processing if reasoning is not finished.
+        # reasoner is not None only when `--reasoning-parser` is set.
+        if self._reasoner is not None:
+            if not self._reasoner.is_reasoning_end(input_ids):
+                return scores
+            else:
+                # Remove the reasoning tokens from the input_ids
+                # We need this because our implementation relies on the
+                # hash of the input_ids to store the FSM state.
+                input_ids = self._reasoner.extract_content_ids(input_ids)
+
+        seq_id = hash(tuple(input_ids))
+
+        if len(input_ids) > 0:
+            last_token = input_ids[-1]
+            last_seq_id = hash(tuple(input_ids[:-1]))
+            self._fsm_state[seq_id] = self._guide.get_next_state(
+                state=self._fsm_state[last_seq_id], token_id=last_token)
+        else:
+            # Note: this is a hack.
+            # Lark pickling does not work properly (silent failure),
+            # which breaks the RPC (which uses python pickleing).
+            # We need to find a better solution.
+            # On the first time this is called, we simply re-create
+            # the Lark object.
+            if isinstance(self._guide, CFGGuide):
+                self._guide.parser = PartialLark(
+                    self._guide.cfg_string,
+                    parser="lalr",
+                    import_paths=[grammars.GRAMMAR_PATH],
+                )
+                self._fsm_state[seq_id] = CFGState(
+                    parser_state=self._guide.parser.parse(""), prev_token=None)
+
+        instruction = self._guide.get_next_instruction(
+            state=self._fsm_state[seq_id])
+
+        if type(instruction) == Generate:  # noqa: E721
+            allowed_tokens = instruction.tokens
+        elif type(instruction) == Write:  # noqa: E721
+            # TODO: support fast forward tokens
+            allowed_tokens = [instruction.tokens[0]]
+        else:
+            raise TypeError(
+                f"Unsupported instruction type {type(instruction)}")
+
+        mask = torch.full((scores.shape[-1], ),
+                          -torch.inf,
+                          device=scores.device)
+        # The tokenizer may support more token ids than the model can generate,
+        # eg. Llama 3.2 Vision models have an `<|image|>` token with id 128256
+        # but scores.shape == torch.Size([128256])
+        # Using NumPy is faster for filtering token ids
+        allowed_tokens = np.array(allowed_tokens, dtype=np.int64)
+        allowed_tokens = torch.tensor(allowed_tokens, device=scores.device)
+        allowed_tokens = allowed_tokens.masked_select(
+            allowed_tokens < scores.shape[-1])
+        mask.index_fill_(0, allowed_tokens, 0)
+        if current_platform.is_hpu():
+            # Workaround for HPU bug where add_() raise RuntimeError:
+            # synNodeCreateWithId failed for node: strided_insert
+            # with synStatus 1 [Invalid argument], hopefully it will
+            # be fixed in the future releases of the HPU runtime.
+            scores = scores.add(mask)
+        else:
+            scores.add_(mask)
+        return scores
+
+
+class RegexLogitsProcessor(BaseLogitsProcessor):
+
+    @classmethod
+    @cache()
+    def _get_guide(cls, regex_string: str,
+                   tokenizer: PreTrainedTokenizerBase) -> Guide:
+        tokenizer = _adapt_tokenizer(tokenizer)
+        return RegexGuide.from_regex(regex_string, tokenizer)
+
+    def __init__(
+        self,
+        regex_string: str,
+        tokenizer: PreTrainedTokenizerBase,
+        reasoner: Optional[ReasoningParser],
+    ):
+        """Compile the FSM that drives the regex-structured generation.
+
+        Parameters
+        ----------
+        regex_string
+            A string that represents a regular expression
+        tokenizer
+            The model's tokenizer
+
+        """
+        super().__init__(
+            RegexLogitsProcessor._get_guide(regex_string, tokenizer), reasoner)
+
+
+class JSONLogitsProcessor(RegexLogitsProcessor):
+
+    def __init__(self, schema: Union[str, dict, BaseModel],
+                 tokenizer: PreTrainedTokenizerBase,
+                 whitespace_pattern: Union[str, None],
+                 reasoner: Optional[ReasoningParser]):
+        """Compile the FSM that drives the JSON-guided generation.
+
+        Parameters
+        ----------
+        schema
+            A JSON schema that encodes the structure we want the model to
+            generate
+        tokenizer
+            The model's tokenizer
+        whitespace_pattern
+            Pattern to use for JSON syntactic whitespace (doesn't impact
+            string literals)
+            Example: allow only a single space or newline with
+            `whitespace_pattern=r"[\n ]?"`
+        """
+        if isinstance(schema, type(BaseModel)):
+            schema_str = json.dumps(schema.model_json_schema())
+        elif isinstance(schema, dict):
+            schema_str = json.dumps(schema)
+        elif isinstance(schema, str):
+            schema_str = schema
+        else:
+            raise ValueError(
+                f"Cannot parse schema {schema}. The schema must be either "
+                f"a Pydantic object, a dictionary or a string that contains "
+                f"the JSON Schema specification")
+        regex_string = build_regex_from_schema(schema_str, whitespace_pattern)
+        super().__init__(regex_string, tokenizer, reasoner)
+
+
+class CFGLogitsProcessor(BaseLogitsProcessor):
+
+    @classmethod
+    @cache()
+    def _get_guide(cls, cfg: str, tokenizer: PreTrainedTokenizerBase) -> Guide:
+        tokenizer = _adapt_tokenizer(tokenizer)
+        return CFGGuide(cfg, tokenizer)
+
+    def __init__(self, cfg: str, tokenizer: PreTrainedTokenizerBase,
+                 reasoner: Optional[ReasoningParser]):
+        """Compile the FSM that drives the context free grammar generation.
+
+        Parameters
+        ----------
+        cfg
+            A string that represents a context-free grammar
+        tokenizer
+            The model's tokenizer
+
+        """
+        super().__init__(CFGLogitsProcessor._get_guide(cfg, tokenizer),
+                         reasoner)
+        self._guide = self._guide.copy()
+
+    def clone(self) -> "CFGLogitsProcessor":
+        cloned = copy.copy(self)
+        cloned._fsm_state = copy.deepcopy(self._fsm_state)
+        cloned._guide = self._guide.copy()
+        return cloned
+
+
+@lru_cache(maxsize=32)
+def _adapt_tokenizer(tokenizer: PreTrainedTokenizerBase):
+    """Adapt vLLM's tokenizer to use to compile the FSM.
+
+    The API of Outlines tokenizers is slightly different to that of
+    `transformers`. The decoder of outlines, returns a list whereas
+    the decode of vLLM returns an str. To sync the vLLM decoder with
+    outlines internal api, the decoder should be adapted. In addition
+    we need to handle the missing spaces to Llama's tokenizer to be
+    able to compile FSMs for this model.
+
+    """
+    if getattr(tokenizer, "_outlines_adapted", False):
+        return tokenizer
+
+    tokenizer = copy.deepcopy(tokenizer)
+
+    tokenizer.vocabulary = tokenizer.get_vocab()
+    tokenizer.special_tokens = set(tokenizer.all_special_tokens)
+
+    def convert_token_to_string(token: str) -> str:
+        from transformers.file_utils import SPIECE_UNDERLINE
+
+        string = tokenizer.convert_tokens_to_string([token])
+
+        # A hack to handle missing spaces to HF's Llama tokenizers
+        if (type(token) is str and token.startswith(SPIECE_UNDERLINE)
+                or token == "<0x20>"):
+            return " " + string
+
+        return string
+
+    def change_decoder(
+        decoder: Callable[[list[int]],
+                          str]) -> Callable[[list[int]], list[str]]:
+        """Sync vLLM's decoder with the outlines by returning list."""
+
+        def new_decoder(inp_tokens: list[int]) -> list[str]:
+            if (isinstance(inp_tokens, list) and len(inp_tokens) == 1
+                    and isinstance(inp_tokens[0], list)):
+                inp_tokens = inp_tokens[0]
+            return [decoder(inp_tokens)]
+
+        return new_decoder
+
+    tokenizer.convert_token_to_string = convert_token_to_string
+    tokenizer.decode = change_decoder(tokenizer.decode)
+    setattr(tokenizer, "_outlines_adapted", True)  # noqa: B010
+
+    return tokenizer

vllm/model_executor/guided_decoding/utils.py

@@ -0,0 +1,242 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import regex as re
+
+
+def has_xgrammar_unsupported_json_features(schema: dict) -> bool:
+    """Check if JSON schema contains features unsupported by xgrammar."""
+
+    def check_object(obj: dict) -> bool:
+        if not isinstance(obj, dict):
+            return False
+
+        # Check for numeric ranges
+        if obj.get("type") in ("integer", "number") and ("multipleOf" in obj):
+            return True
+
+        # Check for array unsupported keywords
+        if obj.get("type") == "array" and any(key in obj for key in [
+                "uniqueItems", "contains", "minContains", "maxContains",
+                "minItems", "maxItems"
+        ]):
+            return True
+
+        # Unsupported keywords for strings
+        if obj.get("type") == "string" and any(
+                key in obj for key in ["minLength", "maxLength", "format"]):
+            return True
+
+        # Unsupported keywords for objects
+        if obj.get("type") == "object" and any(key in obj for key in [
+                "minProperties", "maxProperties", "propertyNames",
+                "patternProperties"
+        ]):
+            return True
+
+        # Recursively check all nested objects and arrays
+        for value in obj.values():
+            if isinstance(value, dict):
+                if check_object(value):
+                    return True
+            elif isinstance(value, list):
+                for item in value:
+                    if isinstance(item, dict) and check_object(item):
+                        return True
+
+        return False
+
+    return check_object(schema)
+
+
+def has_lmf_unsupported_json_features(schema: dict) -> bool:
+    """
+    Check if JSON schema contains features unsupported 
+    by lm_format_enforcer.
+
+    Known issues:
+    - Regex patterns:
+        "grade": {
+            "type": "string",
+            "pattern": "^[A-D]$"  # Regex pattern
+        },
+    """
+
+    def check_object(obj: dict) -> bool:
+        if not isinstance(obj, dict):
+            return False
+
+        # Check for pattern restrictions
+        if "pattern" in obj:
+            return True
+
+        # Recursively check all nested objects and arrays
+        for value in obj.values():
+            if isinstance(value, dict):
+                if check_object(value):
+                    return True
+            elif isinstance(value, list):
+                for item in value:
+                    if isinstance(item, dict) and check_object(item):
+                        return True
+
+        return False
+
+    return check_object(schema)
+
+
+def grammar_is_likely_lark(grammar_str: str) -> bool:
+    """
+    Check if grammar appears to use Lark syntax.
+    
+    Args:
+        grammar_str: Input grammar string
+        
+    Returns:
+        bool: True if grammar appears to be in Lark format, False otherwise
+        
+    Examples:
+        >>> grammar_is_likely_lark("rule: 'abc'")
+        True
+        >>> grammar_is_likely_lark("rule ::= 'abc'")
+        False
+    """
+    if not grammar_str or not isinstance(grammar_str, str):
+        return False
+
+    for line in grammar_str.split('\n'):
+        # Remove both comment styles
+        line = re.sub(r'(#|//).*$', '', line).strip()
+        if not line:
+            continue
+
+        # Look for GBNF rule definition
+        if '::=' in line:
+            return False
+
+    return True
+
+
+def convert_lark_to_gbnf(grammar_str: str) -> str:
+    """
+    Convert a Lark grammar string to GBNF format.
+
+    GBNF reference:
+    https://github.com/ggerganov/llama.cpp/blob/master/grammars/README.md
+    Lark grammar reference:
+    https://lark-parser.readthedocs.io/en/latest/grammar.html
+    
+    Args:
+        grammar_str: Input grammar in Lark format
+        
+    Returns:
+        str: Converted grammar in GBNF format
+        
+    Examples:
+        >>> print(convert_lark_to_gbnf("rule: 'hello'"))
+        root ::= rule
+        rule ::= "hello"
+    """
+    if not isinstance(grammar_str, str):
+        raise ValueError(f"Grammar must be a string, got {type(grammar_str)}")
+    if not grammar_str.strip():
+        raise ValueError("Grammar string cannot be empty")
+
+    defined_rules = set()
+    referenced_rules = set()
+    output_lines = []
+
+    def clean_line(line: str) -> str:
+        """Remove comments and whitespace from line."""
+        return re.sub(r'(#|//).*$', '', line).strip()
+
+    def check_quotes(text: str, rule_name: str, line_num: int) -> None:
+        """Validate quote matching in text."""
+        if text.count("'") % 2 != 0 or text.count('"') % 2 != 0:
+            raise ValueError(
+                f"Mismatched quotes in {rule_name} on line {line_num}")
+
+    def extract_references(text: str) -> set:
+        """Extract rule references from text."""
+        # Remove quoted strings and special characters
+        text = re.sub(r'"[^"]*"', '', text)
+        text = re.sub(r'[+*?()|\[\]{}]', ' ', text)
+        return set(re.findall(r'\b[a-zA-Z_][a-zA-Z0-9_]*\b', text))
+
+    # First pass: Find root rule and validate rule definitions
+    lines = [clean_line(line) for line in grammar_str.split('\n')]
+    first_rule = None
+
+    for line_num, line in enumerate(lines, 1):
+        if not line or line.startswith('|'):
+            continue
+
+        if ':' in line:
+            try:
+                name = line.split(':', 1)[0].strip().strip('?')
+                defined_rules.add(name)
+                if first_rule is None:
+                    first_rule = name
+                if name == 'start':
+                    first_rule = 'start'
+            except IndexError as e:
+                raise ValueError(f"Invalid rule format on line {line_num}. "
+                                 "Expected 'rule_name: definition'") from e
+
+    if not defined_rules:
+        raise ValueError("No valid rules found in grammar")
+
+    # Add root rule
+    output_lines.append(f"root ::= {first_rule}")
+
+    # Second pass: Process rule definitions and alternatives
+    current_rule = None
+    current_definition = []
+
+    for line_num, line in enumerate(lines, 1):
+        if not line:
+            continue
+
+        try:
+            if ':' in line and not line.startswith('|'):
+                # Save previous rule if exists
+                if current_rule:
+                    output_lines.append(
+                        f"{current_rule} ::= {' | '.join(current_definition)}")
+
+                # Process new rule
+                name, definition = line.split(':', 1)
+                current_rule = name.strip().strip('?')
+
+                check_quotes(definition, f"rule '{current_rule}'", line_num)
+                definition = re.sub(r"'([^']*)'", r'"\1"', definition)
+                referenced_rules.update(extract_references(definition))
+                current_definition = [definition.strip()]
+
+            elif line.startswith('|'):
+                if not current_rule:
+                    raise ValueError(f"Alternative '|' on line {line_num} "
+                                     "without a preceding rule definition")
+
+                alt_def = line[1:].strip()
+                check_quotes(alt_def, f"alternative for rule '{current_rule}'",
+                             line_num)
+                alt_def = re.sub(r"'([^']*)'", r'"\1"', alt_def)
+                referenced_rules.update(extract_references(alt_def))
+                current_definition.append(alt_def)
+
+        except ValueError as e:
+            raise ValueError(f"Error on line {line_num}: {str(e)}") from e
+
+    # Add final rule if exists
+    if current_rule:
+        output_lines.append(
+            f"{current_rule} ::= {' | '.join(current_definition)}")
+
+    # Validate all rules are defined
+    undefined_rules = referenced_rules - defined_rules - {'root'}
+    if undefined_rules:
+        raise ValueError("Referenced rules are not defined: "
+                         f"{', '.join(sorted(undefined_rules))}")
+
+    return '\n'.join(output_lines)

vllm/model_executor/guided_decoding/xgrammar_decoding.py

@@ -0,0 +1,426 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+# noqa: UP007
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass, field
+from typing import TYPE_CHECKING, Any
+
+import regex as re
+import torch
+
+import vllm.envs
+from vllm.logger import init_logger
+
+try:
+    import xgrammar as xgr
+    xgr_installed = True
+except ImportError:
+    xgr_installed = False
+    pass
+
+from vllm.model_executor.guided_decoding.utils import (convert_lark_to_gbnf,
+                                                       grammar_is_likely_lark)
+from vllm.transformers_utils.tokenizers.mistral import MistralTokenizer
+
+if TYPE_CHECKING:
+    from transformers import PreTrainedTokenizer
+
+    from vllm.config import ModelConfig
+    from vllm.reasoning import ReasoningParser
+    from vllm.sampling_params import GuidedDecodingParams
+
+logger = init_logger(__name__)
+
+
+def get_local_xgrammar_guided_decoding_logits_processor(
+        guided_params: GuidedDecodingParams,
+        tokenizer: PreTrainedTokenizer,
+        model_config: ModelConfig,
+        reasoner: ReasoningParser | None,
+        max_threads: int = 8):
+    config = GrammarConfig.from_guided_params(guided_params=guided_params,
+                                              model_config=model_config,
+                                              tokenizer=tokenizer,
+                                              max_threads=max_threads)
+    return XGrammarLogitsProcessor(config, reasoner)
+
+
+@dataclass(frozen=True)
+class TokenizerData:
+    """Immutable container for cached tokenizer data."""
+    metadata: str
+    encoded_vocab: list[str] = field(default_factory=list)
+
+
+class TokenizerDataCache:
+    """Cache manager for tokenizer data to avoid repeated processing."""
+    _cache: dict[int, TokenizerData] = {}
+
+    @classmethod
+    def get_tokenizer_data(
+        cls,
+        tokenizer: PreTrainedTokenizer,
+        /,
+        *,
+        tokenizer_hash: int,
+        vocab_size: int,
+    ) -> TokenizerData:
+
+        if tokenizer_hash not in cls._cache:
+            tokenizer_info = xgr.TokenizerInfo.from_huggingface(
+                tokenizer,
+                # NOTE: We will need to use lm_head's vocab_size
+                # to determine correct special_token_ids for this tokenizer.
+                # See https://github.com/mlc-ai/xgrammar/commit/70c959fb6d9cea75aae33c414763cd0602022d92  # noqa: E501
+                vocab_size=vocab_size,
+            )
+            metadata = json.loads(tokenizer_info.dump_metadata())
+
+            # Vendored from xgrammar logic to get encoded_vocab
+            # https://github.com/mlc-ai/xgrammar/blob/989222175c2a30fb7987d8bcce35bec1bf6817f2/python/xgrammar/tokenizer_info.py#L127 # noqa: E501
+            try:
+                vocab_dict = tokenizer.get_vocab()
+            except AttributeError as e:
+                raise ValueError(
+                    f"Cannot get the vocabulary of the tokenizer "
+                    f"{type(tokenizer)}. The tokenizer should have a "
+                    "get_vocab method.") from e
+
+            # maintain tokenizer's indexing
+            encoded_vocab = [""] * tokenizer_info.vocab_size
+            for token, idx in vocab_dict.items():
+                if idx < tokenizer_info.vocab_size:
+                    encoded_vocab[idx] = token
+
+            if isinstance(tokenizer, MistralTokenizer):
+                # REF: https://github.com/mlc-ai/xgrammar/blob/5e141f6ff1ca02bc31f9e512e68b61f2a8ae88e5/tests/python/test_tokenizer_info.py#L43 # noqa: E501
+                metadata.update({
+                    "vocab_type": xgr.VocabType.BYTE_FALLBACK,
+                    "add_prefix_space": True
+                })
+
+            cls._cache[tokenizer_hash] = TokenizerData(
+                encoded_vocab=encoded_vocab,
+                metadata=json.dumps(metadata),
+            )
+
+        return cls._cache[tokenizer_hash]
+
+
+class GrammarCompilerCache:
+    """
+    Cache for GrammarCompiler instances based on tokenizer.
+
+    This cache reduces the overhead of creating new compiler instances when
+    using the same tokenizer configuration.
+    """
+    _cache: dict[str, xgr.GrammarCompiler] = {}
+
+    @classmethod
+    def get_compiler(cls, config: GrammarConfig) -> xgr.GrammarCompiler:
+        cache_key = str(config.tokenizer_hash)
+
+        if cache_key not in cls._cache:
+            config_data = config.tokenizer_data
+
+            # In TokenizerDataCache.get_tokenizer_data, a serializable
+            # tokenizer_data is created and cached. This data is used to build
+            # a tokenizer_info and create an xgrammar compiler.
+            tokenizer_info = xgr.TokenizerInfo.from_vocab_and_metadata(
+                encoded_vocab=config_data.encoded_vocab,
+                metadata=config_data.metadata,
+            )
+            cache_size = vllm.envs.VLLM_XGRAMMAR_CACHE_MB * 1024 * 1024
+            cls._cache[cache_key] = xgr.GrammarCompiler(
+                tokenizer_info,
+                max_threads=config.max_threads,
+                cache_enabled=True,
+                cache_limit_bytes=cache_size,
+            )
+
+        return cls._cache[cache_key]
+
+
+@dataclass
+class GrammarConfig:
+    """Serializable configuration for grammar compilation"""
+    tokenizer_hash: int
+    tokenizer_data: TokenizerData
+    json_str: str | None = None
+    grammar_str: str | None = None
+    json_object: bool | None = None
+    any_whitespace: bool = True
+    regex_str: str | None = None
+    max_threads: int = 8
+
+    @classmethod
+    def from_guided_params(cls,
+                           guided_params: GuidedDecodingParams,
+                           model_config: ModelConfig,
+                           tokenizer: PreTrainedTokenizer,
+                           max_threads: int = 8) -> GrammarConfig:
+
+        tokenizer_hash = hash(tokenizer)
+        tokenizer_data = TokenizerDataCache.get_tokenizer_data(
+            tokenizer,
+            tokenizer_hash=tokenizer_hash,
+            vocab_size=model_config.hf_text_config.vocab_size,
+        )
+
+        if guided_params.json:
+            if not isinstance(guided_params.json, str):
+                json_str = json.dumps(guided_params.json)
+            else:
+                json_str = guided_params.json
+
+            any_whitespace = not guided_params.disable_any_whitespace
+
+            # Check and log if model with xgrammar and whitespace have history
+            # of runaway generation of whitespaces.
+            # References:
+            # https://github.com/vllm-project/vllm/pull/12744
+            # https://github.com/mlc-ai/xgrammar/issues/212
+            model_with_warn = None
+
+            if 'Mistral' in model_config.model:
+                model_with_warn = 'Mistral'
+            elif 'Qwen' in model_config.model:
+                model_with_warn = 'Qwen'
+
+            if model_with_warn is not None and any_whitespace:
+                logger.info_once(
+                    "%s model detected, consider setting `disable_any_whitespace` to prevent runaway generation of whitespaces.",  # noqa: E501
+                    model_with_warn,
+                )
+            # Validate the schema and raise ValueError here if it is invalid.
+            # This is to avoid exceptions in model execution, which will crash
+            # the engine worker process.
+            try:
+                xgr.Grammar.from_json_schema(json_str,
+                                             any_whitespace=any_whitespace)
+            except RuntimeError as err:
+                raise ValueError(str(err)) from err
+
+            return cls(json_str=json_str,
+                       tokenizer_hash=tokenizer_hash,
+                       max_threads=max_threads,
+                       tokenizer_data=tokenizer_data,
+                       any_whitespace=any_whitespace)
+        elif guided_params.grammar:
+            # XGrammar only supports GBNF grammars, so we must convert Lark
+            if grammar_is_likely_lark(guided_params.grammar):
+                try:
+                    grammar_str = convert_lark_to_gbnf(guided_params.grammar)
+                except ValueError as e:
+                    raise ValueError(
+                        "Failed to convert the grammar from Lark to GBNF. "
+                        "Please either use GBNF grammar directly or specify"
+                        " --guided-decoding-backend=outlines.\n"
+                        f"Conversion error: {str(e)}") from e
+            else:
+                grammar_str = guided_params.grammar
+
+            # Validate the grammar and raise ValueError here if it is invalid.
+            # This is to avoid exceptions in model execution, which will crash
+            # the engine worker process.
+            try:
+                xgr.Grammar.from_ebnf(grammar_str)
+            except RuntimeError as err:
+                raise ValueError(str(err)) from err
+
+            return cls(grammar_str=grammar_str,
+                       tokenizer_hash=tokenizer_hash,
+                       max_threads=max_threads,
+                       tokenizer_data=tokenizer_data)
+        elif guided_params.json_object:
+            return cls(
+                json_object=True,
+                tokenizer_hash=tokenizer_hash,
+                max_threads=max_threads,
+                tokenizer_data=tokenizer_data,
+            )
+        elif guided_params.choice:
+            choice_str = GrammarConfig.choice_as_grammar(guided_params.choice)
+            try:
+                xgr.Grammar.from_ebnf(choice_str)
+            except RuntimeError as err:
+                raise ValueError(str(err)) from err
+
+            return cls(
+                grammar_str=choice_str,
+                tokenizer_hash=tokenizer_hash,
+                max_threads=max_threads,
+                tokenizer_data=tokenizer_data,
+            )
+        elif guided_params.regex:
+            return cls(
+                regex_str=guided_params.regex,
+                tokenizer_hash=tokenizer_hash,
+                max_threads=max_threads,
+                tokenizer_data=tokenizer_data,
+            )
+        else:
+            raise ValueError(
+                "Currently only support JSON and EBNF grammar mode for xgrammar"
+            )
+
+    @staticmethod
+    def escape_ebnf_string(s: str) -> str:
+        """Escape special characters in a EBNF string."""
+        # Escape double quotes and backslashes
+        return re.sub(r'(["\\])', r'\\\1', s)
+
+    @staticmethod
+    def choice_as_grammar(choice: list[str] | None) -> str:
+        if choice is None:
+            raise ValueError("Choice is not set")
+        escaped_choices = (GrammarConfig.escape_ebnf_string(c) for c in choice)
+        grammar = ('root ::= ' + ' | '.join(f'"{c}"' for c in escaped_choices))
+        return grammar
+
+    @staticmethod
+    def tokenizer_info(tokenizer_data: TokenizerData) -> xgr.TokenizerInfo:
+        return xgr.TokenizerInfo.from_vocab_and_metadata(
+            encoded_vocab=tokenizer_data.encoded_vocab,
+            metadata=tokenizer_data.metadata,
+        )
+
+
+@dataclass
+class XGrammarLogitsProcessor:
+    """Wrapper class to support pickle protocol"""
+    config: GrammarConfig
+    reasoner: ReasoningParser | None = None
+
+    ctx: xgr.CompiledGrammar | None = None
+    tokenizer_info: xgr.TokenizerInfo = None  # type: ignore[assignment]
+    token_bitmask: torch.Tensor = None  # type: ignore[assignment]
+    matchers: list[xgr.GrammarMatcher] = field(default_factory=list)
+    batch_size: int = field(default=1)
+    prefilled: bool = field(default=False)
+
+    def __post_init__(self):
+        if self.tokenizer_info is None:
+            self.tokenizer_info = self.config.tokenizer_info(
+                self.config.tokenizer_data)
+
+    def __getstate__(self) -> dict[str, Any]:
+        return {'config': self.config, 'reasoner': self.reasoner}
+
+    def __setstate__(self, state: dict[str, Any]):
+        self.config = state['config']
+        self.reasoner = state['reasoner']
+
+        self.tokenizer_info = GrammarConfig.tokenizer_info(
+            self.config.tokenizer_data)
+        self.ctx = None
+        self.matchers = []
+        self.batch_size = 1
+        self.token_bitmask = None  # type: ignore[assignment]
+        self.prefilled = False
+
+    def _ensure_ctx(self):
+        """Lazily initialize the processor in the worker process"""
+        if self.ctx is None:
+            compiler = GrammarCompilerCache.get_compiler(self.config)
+            if self.config.json_str is not None:
+                any_whitespace = self.config.any_whitespace
+                self.ctx = compiler\
+                    .compile_json_schema(self.config.json_str,
+                                         any_whitespace=any_whitespace)
+            elif self.config.grammar_str is not None:
+                self.ctx = compiler.compile_grammar(self.config.grammar_str)
+            elif self.config.json_object:
+                any_whitespace = self.config.any_whitespace
+                self.ctx = compiler\
+                    .compile_json_schema('{"type": "object"}',
+                                         any_whitespace=any_whitespace)
+            elif self.config.regex_str:
+                self.ctx = compiler.compile_regex(self.config.regex_str)
+            else:
+                raise ValueError(
+                    "Invalid configuration for xgrammar logits processor")
+
+    def __call__(self, input_ids: list[int],
+                 scores: torch.Tensor) -> torch.Tensor:
+
+        # Skip the structured logits processing if reasoning is not finished.
+        # reasoner is not None only when `--reasoning-parser` is set.
+        if self.reasoner is not None and \
+        not self.reasoner.is_reasoning_end(
+                input_ids):
+            return scores
+
+        if self.ctx is None:
+            self._ensure_ctx()
+
+        if len(self.matchers) == 0:
+            self.matchers = [
+                xgr.GrammarMatcher(self.ctx) for _ in range(self.batch_size)
+            ]
+            self.token_bitmask = xgr.allocate_token_bitmask(
+                self.batch_size, self.tokenizer_info.vocab_size)
+
+        if not self.prefilled:
+            # Have not sampled a token yet
+            self.prefilled = True
+        else:
+            for i, matcher in enumerate(self.matchers):
+                if not matcher.is_terminated():
+                    sampled_token = input_ids[-1]
+                    assert self.matchers[i].accept_token(sampled_token)
+
+        for i, matcher in enumerate(self.matchers):
+            if not matcher.is_terminated():
+                # @ubospica: ideally, fill_next_token_bitmask should be
+                # parallelized with model decoding
+                # See https://github.com/vllm-project/vllm/pull/10785/files#r1864278303
+                matcher.fill_next_token_bitmask(self.token_bitmask, i)
+
+        # token_bitmask is a CPU tensor for use with accept_token and
+        # fill_next_token_bitmask so we move it to the device of scores
+        device_type = scores.device.type
+        dtype = scores.dtype
+        if device_type != "cuda":
+            # xgrammar on cpu only supports float32 scores
+            # see: https://github.com/mlc-ai/xgrammar/blob/c1b64920cad24f44f235778c1c00bb52d57da01a/python/xgrammar/kernels/apply_token_bitmask_inplace_cpu.py#L22
+            scores = scores.to("cpu").float().unsqueeze(0)
+
+        # Note: In this method, if the tensors have different dimensions
+        # on CPU device fails, but on GPU it runs without error. Hence the
+        # unsqueeze above for scores, to match the token bitmask shape
+        xgr.apply_token_bitmask_inplace(
+            scores, self.token_bitmask.to(scores.device, non_blocking=True))
+        if device_type != "cuda":
+            scores = scores.to(dtype).to(device_type).squeeze()
+
+        return scores
+
+    def clone(self) -> XGrammarLogitsProcessor:
+        """Create a new instance with shared compiled grammar
+          but separate state"""
+        new_processor = XGrammarLogitsProcessor(self.config, self.reasoner,
+                                                None, self.tokenizer_info)
+
+        # Share the compiled grammar context (immutable after compilation)
+        new_processor.ctx = self.ctx
+
+        # Create fresh matchers for the new sequence
+        if self.ctx is not None:
+            new_processor.matchers = [
+                xgr.GrammarMatcher(self.ctx) for _ in range(self.batch_size)
+            ]
+
+        # Create a new token bitmask with the same size
+        if hasattr(self, 'token_bitmask') and self.token_bitmask is not None:
+            new_processor.token_bitmask = self.token_bitmask
+
+        # Copy simple attributes
+        new_processor.batch_size = self.batch_size
+        # Reset prefilled state for new sequence
+        new_processor.prefilled = False
+
+        return new_processor

vllm/model_executor/layers/activation.py

@@ -0,0 +1,369 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+"""Custom activation functions."""
+import math
+from typing import Optional
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from vllm.distributed import (divide, get_tensor_model_parallel_rank,
+                              get_tensor_model_parallel_world_size)
+from vllm.model_executor.custom_op import CustomOp
+from vllm.model_executor.utils import set_weight_attrs
+from vllm.platforms import current_platform
+from vllm.utils import LazyDict
+
+
+@CustomOp.register("fatrelu_and_mul")
+class FatreluAndMul(CustomOp):
+    """An activation function for FATReLU.
+
+    The function computes x -> FATReLU(x[:d]) * x[d:] where
+    d = x.shape[-1] // 2.
+    This is used in openbmb/MiniCPM-S-1B-sft.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self, threshold: float = 0.):
+        super().__init__()
+        self.threshold = threshold
+        if current_platform.is_cuda_alike():
+            self.op = torch.ops._C.fatrelu_and_mul
+        elif current_platform.is_cpu():
+            self._forward_method = self.forward_native
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        x1 = x[..., :d]
+        x2 = x[..., d:]
+        x1 = F.threshold(x1, self.threshold, 0.0)
+        return x1 * x2
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x, self.threshold)
+        return out
+
+
+@CustomOp.register("silu_and_mul")
+class SiluAndMul(CustomOp):
+    """An activation function for SwiGLU.
+
+    The function computes x -> silu(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda_alike() or current_platform.is_cpu():
+            self.op = torch.ops._C.silu_and_mul
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            self.op = ipex_ops.silu_and_mul
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return F.silu(x[..., :d]) * x[..., d:]
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x)
+        return out
+
+    def forward_neuron(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        x_reshaped = x.view(-1, x.shape[-1])
+        s = x_reshaped[:, :d] * F.sigmoid(x_reshaped[:, :d])
+        result = s * x_reshaped[:, d:]
+        return result.view(*x.shape[:-1], d)
+
+
+@CustomOp.register("mul_and_silu")
+class MulAndSilu(CustomOp):
+    """An activation function for SwiGLU.
+
+    The function computes x -> x[:d] * silu(x[d:]) where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (num_tokens, 2 * d) or (batch_size, seq_len, 2 * d)
+        return: (num_tokens, d) or (batch_size, seq_len, d)
+    """
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda_alike():
+            self.op = torch.ops._C.mul_and_silu
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            self.op = ipex_ops.silu_and_mul
+        elif current_platform.is_cpu():
+            self._forward_method = self.forward_native
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return x[..., :d] * F.silu(x[..., d:])
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x)
+        return out
+
+    # TODO implement forward_xpu for MulAndSilu
+    # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+
+
+@CustomOp.register("gelu_and_mul")
+class GeluAndMul(CustomOp):
+    """An activation function for GeGLU.
+
+    The function computes x -> GELU(x[:d]) * x[d:] where d = x.shape[-1] // 2.
+
+    Shapes:
+        x: (batch_size, seq_len, 2 * d) or (num_tokens, 2 * d)
+        return: (batch_size, seq_len, d) or (num_tokens, d)
+    """
+
+    def __init__(self, approximate: str = "none"):
+        super().__init__()
+        self.approximate = approximate
+        if approximate not in ("none", "tanh"):
+            raise ValueError(f"Unknown approximate mode: {approximate}")
+        if current_platform.is_cuda_alike() or current_platform.is_cpu():
+            if approximate == "none":
+                self.op = torch.ops._C.gelu_and_mul
+            elif approximate == "tanh":
+                self.op = torch.ops._C.gelu_tanh_and_mul
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            if approximate == "none":
+                self.op = ipex_ops.gelu_and_mul
+            else:
+                self.op = ipex_ops.gelu_tanh_and_mul
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        d = x.shape[-1] // 2
+        return F.gelu(x[..., :d], approximate=self.approximate) * x[..., d:]
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        d = x.shape[-1] // 2
+        output_shape = (x.shape[:-1] + (d, ))
+        out = torch.empty(output_shape, dtype=x.dtype, device=x.device)
+        self.op(out, x)
+        return out
+
+    def extra_repr(self) -> str:
+        return f'approximate={repr(self.approximate)}'
+
+
+@CustomOp.register("gelu_new")
+class NewGELU(CustomOp):
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda_alike() or current_platform.is_cpu():
+            self.op = torch.ops._C.gelu_new
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            self.op = ipex_ops.gelu_new
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        c = math.sqrt(2.0 / math.pi)
+        return 0.5 * x * (1.0 + torch.tanh(c *
+                                           (x + 0.044715 * torch.pow(x, 3.0))))
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        out = torch.empty_like(x)
+        self.op(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        return self.op(x)
+
+
+@CustomOp.register("gelu_fast")
+class FastGELU(CustomOp):
+
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda_alike() or current_platform.is_cpu():
+            self.op = torch.ops._C.gelu_fast
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            self.op = ipex_ops.gelu_fast
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return 0.5 * x * (1.0 + torch.tanh(x * 0.7978845608 *
+                                           (1.0 + 0.044715 * x * x)))
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        out = torch.empty_like(x)
+        self.op(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        return self.op(x)
+
+
+@CustomOp.register("quick_gelu")
+class QuickGELU(CustomOp):
+    # https://github.com/huggingface/transformers/blob/main/src/transformers/activations.py#L90
+    def __init__(self):
+        super().__init__()
+        if current_platform.is_cuda_alike() or current_platform.is_cpu():
+            self.op = torch.ops._C.gelu_quick
+        elif current_platform.is_xpu():
+            from vllm._ipex_ops import ipex_ops
+            self.op = ipex_ops.gelu_quick
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return x * torch.sigmoid(1.702 * x)
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        out = torch.empty_like(x)
+        self.op(out, x)
+        return out
+
+    def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+        out = torch.empty_like(x)
+        self.op(out, x)
+        return out
+
+    # TODO implement forward_xpu for QuickGELU
+    # def forward_xpu(self, x: torch.Tensor) -> torch.Tensor:
+
+
+@CustomOp.register("relu2")
+class ReLUSquaredActivation(CustomOp):
+    """
+    Applies the relu^2 activation introduced in https://arxiv.org/abs/2109.08668v2
+    """
+
+    def forward_native(self, x: torch.Tensor) -> torch.Tensor:
+        """PyTorch-native implementation equivalent to forward()."""
+        return torch.square(F.relu(x))
+
+    def forward_cuda(self, x: torch.Tensor) -> torch.Tensor:
+        return self.forward_native(x)
+
+
+class ScaledActivation(nn.Module):
+    """An activation function with post-scale parameters.
+
+    This is used for some quantization methods like AWQ.
+    """
+
+    def __init__(
+        self,
+        act_module: nn.Module,
+        intermediate_size: int,
+        input_is_parallel: bool = True,
+        params_dtype: Optional[torch.dtype] = None,
+    ):
+        super().__init__()
+        self.act = act_module
+        self.input_is_parallel = input_is_parallel
+        if input_is_parallel:
+            tp_size = get_tensor_model_parallel_world_size()
+            intermediate_size_per_partition = divide(intermediate_size,
+                                                     tp_size)
+        else:
+            intermediate_size_per_partition = intermediate_size
+        if params_dtype is None:
+            params_dtype = torch.get_default_dtype()
+        self.scales = nn.Parameter(
+            torch.empty(intermediate_size_per_partition, dtype=params_dtype))
+        set_weight_attrs(self.scales, {"weight_loader": self.weight_loader})
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return self.act(x) / self.scales
+
+    def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor):
+        param_data = param.data
+        if self.input_is_parallel:
+            tp_rank = get_tensor_model_parallel_rank()
+            shard_size = param_data.shape[0]
+            start_idx = tp_rank * shard_size
+            loaded_weight = loaded_weight.narrow(0, start_idx, shard_size)
+        assert param_data.shape == loaded_weight.shape
+        param_data.copy_(loaded_weight)
+
+
+_ACTIVATION_REGISTRY = LazyDict({
+    "gelu":
+    lambda: nn.GELU(),
+    "gelu_fast":
+    lambda: FastGELU(),
+    "gelu_new":
+    lambda: NewGELU(),
+    "gelu_pytorch_tanh":
+    lambda: nn.GELU(approximate="tanh"),
+    "relu":
+    lambda: nn.ReLU(),
+    "relu2":
+    lambda: ReLUSquaredActivation(),
+    "silu":
+    lambda: nn.SiLU(),
+    "quick_gelu":
+    lambda: QuickGELU(),
+})
+
+
+def get_act_fn(act_fn_name: str) -> nn.Module:
+    """Get an activation function by name."""
+    act_fn_name = act_fn_name.lower()
+    if act_fn_name not in _ACTIVATION_REGISTRY:
+        raise ValueError(
+            f"Activation function {act_fn_name!r} is not supported.")
+
+    return _ACTIVATION_REGISTRY[act_fn_name]
+
+
+_ACTIVATION_AND_MUL_REGISTRY = LazyDict({
+    "gelu": lambda: GeluAndMul(),
+    "silu": lambda: SiluAndMul(),
+    "geglu": lambda: GeluAndMul(),
+})
+
+
+def get_act_and_mul_fn(act_fn_name: str) -> nn.Module:
+    """Get an activation-and-mul (i.e. SiluAndMul) function by name."""
+    act_fn_name = act_fn_name.lower()
+    if act_fn_name not in _ACTIVATION_AND_MUL_REGISTRY:
+        raise ValueError(
+            f"Activation function {act_fn_name!r} is not supported.")
+
+    return _ACTIVATION_AND_MUL_REGISTRY[act_fn_name]

vllm/model_executor/layers/fused_moe/__init__.py

@@ -0,0 +1,54 @@
+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+from contextlib import contextmanager
+from typing import Any, Optional
+
+from vllm.model_executor.layers.fused_moe.layer import (
+    FusedMoE, FusedMoEMethodBase, FusedMoeWeightScaleSupported)
+from vllm.triton_utils import HAS_TRITON
+
+_config: Optional[dict[str, Any]] = None
+
+
+@contextmanager
+def override_config(config):
+    global _config
+    old_config = _config
+    _config = config
+    yield
+    _config = old_config
+
+
+def get_config() -> Optional[dict[str, Any]]:
+    return _config
+
+
+__all__ = [
+    "FusedMoE",
+    "FusedMoEMethodBase",
+    "FusedMoeWeightScaleSupported",
+    "override_config",
+    "get_config",
+]
+
+if HAS_TRITON:
+    # import to register the custom ops
+    import vllm.model_executor.layers.fused_moe.fused_marlin_moe  # noqa
+    import vllm.model_executor.layers.fused_moe.fused_moe  # noqa
+    from vllm.model_executor.layers.fused_moe.cutlass_moe import (
+        cutlass_moe_fp4, cutlass_moe_fp8)
+    from vllm.model_executor.layers.fused_moe.fused_moe import (
+        TritonExperts, fused_experts, fused_moe, fused_topk,
+        get_config_file_name, grouped_topk)
+
+    __all__ += [
+        "fused_moe",
+        "fused_topk",
+        "fused_experts",
+        "get_config_file_name",
+        "grouped_topk",
+        "cutlass_moe_fp8",
+        "cutlass_moe_fp4",
+        "TritonExperts",
+    ]

vllm/model_executor/layers/fused_moe/batched_deep_gemm_moe.py

@@ -0,0 +1,125 @@
+# SPDX-License-Identifier: Apache-2.0
+import importlib.util
+from typing import Optional
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.logger import init_logger
+from vllm.model_executor.layers.fused_moe.utils import (
+    _resize_cache, per_token_group_quant_fp8)
+
+logger = init_logger(__name__)
+
+has_deep_gemm = importlib.util.find_spec("deep_gemm") is not None
+
+
+class BatchedDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
+
+    # The Deep Gemm kernels only support block size of 128
+    DEEPGEMM_BLOCK_SHAPE = 128
+
+    def __init__(self, max_num_tokens: int, world_size: int, dp_size: int,
+                 block_shape: list[int]):
+        """
+        max_num_tokens: Maximum number of tokens from a DP Rank
+        world_size: Number of EP ranks
+        dp_size: Number of data-parallel ranks
+        block_shape: Block quantization block shape
+        """
+        super().__init__()
+        self.max_num_tokens = max_num_tokens
+        self.world_size = world_size
+        self.dp_size = dp_size
+        self.block_shape = block_shape
+
+        assert (len(self.block_shape) == 2 and all(
+            [v == self.DEEPGEMM_BLOCK_SHAPE for v in self.block_shape]))
+
+    def workspace_shapes(
+        self,
+        a: torch.Tensor,
+        aq: torch.Tensor,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        num_experts: int,
+    ) -> tuple[int, int, torch.dtype]:
+        assert a.dim() == 2
+        num_dp = self.world_size // self.dp_size
+        max_num_tokens = a.size(
+            0) if self.max_num_tokens is None else self.max_num_tokens
+        workspace13 = num_experts * max_num_tokens * num_dp * max(K, N)
+        workspace2 = num_experts * max_num_tokens * num_dp * (N // 2)
+        return (workspace13, workspace2, a.dtype)
+
+    def apply(
+        self,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: str,
+        global_num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        w1_scale: Optional[torch.Tensor],
+        w2_scale: Optional[torch.Tensor],
+        w1_zp: Optional[torch.Tensor],
+        w2_zp: Optional[torch.Tensor],
+        a1q_scale: Optional[torch.Tensor],
+        a2_scale: Optional[torch.Tensor],
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_num_tokens: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        import deep_gemm as dg
+        assert hidden_states.ndim == 3
+
+        a1q = hidden_states
+        _, N, K = w1.size()
+
+        if global_num_experts == -1:
+            global_num_experts = w1.size(0)
+
+        assert w2.size(1) == K
+
+        E, max_num_tokens, N, K, top_k_num = mk._moe_problem_size(
+            hidden_states, w1, w2, topk_ids)
+
+        workspace1 = _resize_cache(workspace13, (E, max_num_tokens, N))
+        workspace2 = _resize_cache(workspace2, (E, max_num_tokens, N // 2))
+        workspace3 = _resize_cache(workspace13, (E, max_num_tokens, K))
+
+        # (from deepgemm docs) : A value hint (which is a value on CPU)
+        # for the M expectation of each batch, correctly setting this value
+        # may lead to better performance.
+        expected_m = max_num_tokens
+
+        dg.m_grouped_gemm_fp8_fp8_bf16_nt_masked((a1q, a1q_scale),
+                                                 (w1, w1_scale),
+                                                 out=workspace1,
+                                                 masked_m=expert_num_tokens,
+                                                 expected_m=expected_m)
+
+        # TODO (varun) [Optimization]: Use a batched version of activation.
+        # Similarly for the quant below.
+        self.activation(activation, workspace2, workspace1.view(-1, N))
+
+        w2_hidden_size = workspace2.size(-1)
+        workspace2 = workspace2.view(-1, w2_hidden_size)
+
+        a2q_scale: Optional[torch.Tensor] = None
+        a2q, a2q_scale = per_token_group_quant_fp8(workspace2,
+                                                   self.block_shape[1],
+                                                   column_major_scales=False)
+        a2q = a2q.view(E, max_num_tokens, -1)
+        a2q_scale = a2q_scale.view(E, max_num_tokens, -1)
+
+        dg.m_grouped_gemm_fp8_fp8_bf16_nt_masked((a2q, a2q_scale),
+                                                 (w2, w2_scale),
+                                                 out=workspace3,
+                                                 masked_m=expert_num_tokens,
+                                                 expected_m=expected_m)
+
+        return workspace3

vllm/model_executor/layers/fused_moe/batched_triton_or_deep_gemm_moe.py

@@ -0,0 +1,117 @@
+# SPDX-License-Identifier: Apache-2.0
+from typing import Optional
+
+import torch
+
+import vllm.model_executor.layers.fused_moe.modular_kernel as mk
+from vllm.model_executor.layers.fused_moe.batched_deep_gemm_moe import (
+    BatchedDeepGemmExperts)
+from vllm.model_executor.layers.fused_moe.fused_batched_moe import (
+    BatchedTritonExperts)
+
+
+class BatchedTritonOrDeepGemmExperts(mk.FusedMoEPermuteExpertsUnpermute):
+
+    def __init__(self,
+                 max_num_tokens: int,
+                 world_size: int,
+                 dp_size: int,
+                 use_fp8_w8a8: bool = False,
+                 use_int8_w8a8: bool = False,
+                 use_int8_w8a16: bool = False,
+                 use_int4_w4a16: bool = False,
+                 per_channel_quant: bool = False,
+                 block_shape: Optional[list[int]] = None,
+                 allow_deep_gemm: bool = False):
+        super().__init__()
+        assert not use_int8_w8a8, "NYI"
+        assert not use_int8_w8a16, "NYI"
+        assert not use_int4_w4a16, "NYI"
+
+        self.max_num_tokens = max_num_tokens
+        self.world_size = world_size
+        self.dp_size = dp_size
+        self.use_fp8_w8a8 = use_fp8_w8a8
+        self.use_int8_w8a8 = use_int8_w8a8
+        self.use_int8_w8a16 = use_int8_w8a16
+        self.use_int4_w4a16 = use_int4_w4a16
+        self.per_channel_quant = per_channel_quant
+        self.block_shape = block_shape
+        self.allow_deep_gemm = allow_deep_gemm
+
+        # BatchedTritonKernel doesn't support block quantization
+        # at the moment.
+        self.batched_triton_experts = BatchedTritonExperts(
+            max_num_tokens=self.max_num_tokens,
+            use_fp8_w8a8=self.use_fp8_w8a8,
+            use_int8_w8a8=self.use_int8_w8a8,
+            use_int8_w8a16=self.use_int8_w8a16,
+            use_int4_w4a16=self.use_int4_w4a16,
+            per_channel_quant=self.per_channel_quant,
+            block_shape=self.block_shape,
+            world_size=self.world_size,
+            dp_size=self.dp_size) if self.block_shape is None else None
+
+        is_fp8_128_block_quantized = (self.use_fp8_w8a8
+                                      and self.block_shape is not None
+                                      and len(self.block_shape) == 2 and all(
+                                          [b == 128
+                                           for b in self.block_shape]))
+        self.batched_deep_gemm_experts = BatchedDeepGemmExperts(
+            max_num_tokens=self.max_num_tokens,
+            world_size=self.world_size,
+            dp_size=self.dp_size,
+            block_shape=self.block_shape,  # type: ignore[arg-type]
+        ) if (self.allow_deep_gemm and is_fp8_128_block_quantized) else None
+
+    def workspace_shapes(
+        self,
+        a: torch.Tensor,
+        aq: torch.Tensor,
+        M: int,
+        N: int,
+        K: int,
+        topk: int,
+        num_experts: int,
+    ) -> tuple[int, int, torch.dtype]:
+        # Note: the deep gemm workspaces are strictly larger than the triton
+        # workspaces so we can be pessimistic here and allocate for DeepGemm
+        # even if we fall back to triton later, e.g. if expert maps are set.
+        if self.allow_deep_gemm and self.batched_deep_gemm_experts is not None:
+            return self.batched_deep_gemm_experts.workspace_shapes(
+                a, aq, M, N, K, topk, num_experts)
+        else:
+            assert self.batched_triton_experts is not None
+            return self.batched_triton_experts.workspace_shapes(
+                a, aq, M, N, K, topk, num_experts)
+
+    def apply(
+        self,
+        hidden_states: torch.Tensor,
+        w1: torch.Tensor,
+        w2: torch.Tensor,
+        topk_ids: torch.Tensor,
+        activation: str,
+        global_num_experts: int,
+        expert_map: Optional[torch.Tensor],
+        w1_scale: Optional[torch.Tensor],
+        w2_scale: Optional[torch.Tensor],
+        w1_zp: Optional[torch.Tensor],
+        w2_zp: Optional[torch.Tensor],
+        a1q_scale: Optional[torch.Tensor],
+        a2_scale: Optional[torch.Tensor],
+        workspace13: torch.Tensor,
+        workspace2: torch.Tensor,
+        expert_num_tokens: Optional[torch.Tensor],
+    ) -> torch.Tensor:
+        use_batched_deep_gemm_experts = (self.allow_deep_gemm
+                                         and self.batched_deep_gemm_experts
+                                         is not None)
+        experts = (self.batched_deep_gemm_experts
+                   if use_batched_deep_gemm_experts else
+                   self.batched_triton_experts)
+        assert experts is not None
+        return experts.apply(hidden_states, w1, w2, topk_ids, activation,
+                             global_num_experts, expert_map, w1_scale,
+                             w2_scale, w1_zp, w2_zp, a1q_scale, a2_scale,
+                             workspace13, workspace2, expert_num_tokens)

vllm/model_executor/layers/fused_moe/configs/E=1,N=14336,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "48": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=14336,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "256": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=1792,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "9216": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=1792,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "256": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "9216": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
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+    "BLOCK_SIZE_N": 64,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
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+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
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+  },
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+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
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+  },
+  "2048": {
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+    "BLOCK_SIZE_K": 64,
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+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
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+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
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+  },
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+    "GROUP_SIZE_M": 16,
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+    "num_stages": 4
+  },
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+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
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+  },
+  "9216": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
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+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 64,
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+    "GROUP_SIZE_M": 32,
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+    "num_stages": 4
+  },
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+    "GROUP_SIZE_M": 16,
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+  },
+  "25600": {
+    "BLOCK_SIZE_M": 64,
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+    "num_warps": 4,
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+  },
+  "33792": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
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+  },
+  "41984": {
+    "BLOCK_SIZE_M": 64,
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+    "GROUP_SIZE_M": 32,
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+    "num_stages": 4
+  },
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+  },
+  "58368": {
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
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+    "GROUP_SIZE_M": 1,
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+    "num_stages": 2
+  },
+  "256": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 64,
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+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 256,
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+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
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+    "num_stages": 3
+  },
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+    "BLOCK_SIZE_N": 128,
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+  },
+  "2048": {
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+    "num_warps": 8,
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+  },
+  "3072": {
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+  },
+  "4096": {
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+    "GROUP_SIZE_M": 1,
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+  },
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+  },
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+  },
+  "13312": {
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+    "num_stages": 3
+  },
+  "17408": {
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+  },
+  "25600": {
+    "BLOCK_SIZE_M": 256,
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+  },
+  "33792": {
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+  },
+  "41984": {
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+  },
+  "50176": {
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+    "BLOCK_SIZE_N": 128,
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+    "num_stages": 3
+  },
+  "58368": {
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+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=3072,device_name=NVIDIA_H100_80GB_HBM3.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "32": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
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+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
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+    "GROUP_SIZE_M": 1,
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+    "num_stages": 5
+  },
+  "96": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "256": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "1536": {
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+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "9216": {
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+    "BLOCK_SIZE_K": 64,
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+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 128,
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+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "17408": {
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 128,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 128,
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+    "BLOCK_SIZE_K": 64,
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+    "num_stages": 3
+  },
+  "50176": {
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+    "GROUP_SIZE_M": 16,
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+  },
+  "58368": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=3584,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 64,
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+    "GROUP_SIZE_M": 32,
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+    "num_stages": 4
+  },
+  "9216": {
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+    "BLOCK_SIZE_K": 64,
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+    "num_stages": 4
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
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+  },
+  "33792": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
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+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
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+    "num_stages": 4
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=3584,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "32": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "48": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
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+    "num_stages": 4
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
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+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
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+    "GROUP_SIZE_M": 16,
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+    "num_stages": 4
+  },
+  "1536": {
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+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
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+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
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+    "GROUP_SIZE_M": 32,
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+    "num_stages": 3
+  },
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
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+    "num_warps": 4,
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+  },
+  "9216": {
+    "BLOCK_SIZE_M": 128,
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+    "BLOCK_SIZE_K": 64,
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+    "num_stages": 3
+  },
+  "13312": {
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+    "num_stages": 3
+  },
+  "17408": {
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+    "BLOCK_SIZE_N": 128,
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+    "GROUP_SIZE_M": 16,
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+    "num_stages": 3
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
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+    "num_stages": 3
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 128,
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+    "num_stages": 3
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 128,
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+  },
+  "50176": {
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+  },
+  "58368": {
+    "BLOCK_SIZE_M": 128,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=7168,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
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+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "9216": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=1,N=7168,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "64": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "96": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "128": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "9216": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=1024,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8.json

@@ -0,0 +1,164 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "4": {
+        "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
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "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
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "32": {
+        "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
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "96": {
+        "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
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "256": {
+        "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
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=1024,device_name=AMD_Instinct_MI300X.json

@@ -0,0 +1,200 @@
+{
+    "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": 2
+    },
+    "2": {
+        "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
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "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": 128,
+        "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": 1
+    },
+    "32": {
+        "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
+    },
+    "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": 4,
+        "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": 1,
+        "num_warps": 1,
+        "num_stages": 2,
+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 1
+    },
+    "128": {
+        "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
+    },
+    "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": 16,
+        "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
+    },
+    "1024": {
+        "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
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 32,
+        "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": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 2
+    },
+    "3072": {
+        "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": 1
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "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": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=192,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=192,device_name=NVIDIA_H100_80GB_HBM3.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=192,device_name=NVIDIA_H20-3e.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=192,device_name=NVIDIA_H20.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=192,device_name=NVIDIA_H200.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=384,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,164 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "8": {
+        "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
+    },
+    "16": {
+        "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
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "48": {
+        "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
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "96": {
+        "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
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "512": {
+        "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
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=384,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=384,device_name=NVIDIA_H20-3e.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=384,device_name=NVIDIA_H20.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=384,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=384,device_name=NVIDIA_H200.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=512,device_name=NVIDIA_H100_80GB_HBM3.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=768,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,164 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
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+    },
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+        "waves_per_eu": 0
+    },
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+    },
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+    },
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+    },
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+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
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+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
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+    },
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+    },
+    "1024": {
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+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
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+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
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+        "BLOCK_SIZE_N": 128,
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+        "GROUP_SIZE_M": 4,
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+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2048": {
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+        "GROUP_SIZE_M": 4,
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+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
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+    },
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+        "num_stages": 2,
+        "waves_per_eu": 0
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=768,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
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+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
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+        "num_stages": 4
+    },
+    "24": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
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+        "BLOCK_SIZE_M": 16,
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+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
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+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
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+        "GROUP_SIZE_M": 32,
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+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
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+    },
+    "128": {
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+    },
+    "256": {
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+        "GROUP_SIZE_M": 16,
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+    },
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+        "BLOCK_SIZE_K": 128,
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+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
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+        "GROUP_SIZE_M": 16,
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+        "num_stages": 3
+    },
+    "1536": {
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+        "BLOCK_SIZE_N": 128,
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+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
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+    },
+    "3072": {
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+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=768,device_name=NVIDIA_H20.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
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+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
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+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
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+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 64,
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+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
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+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=768,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=768,device_name=NVIDIA_H200.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=128,N=96,device_name=NVIDIA_H20.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1024,device_name=AMD_Instinct_MI300X.json

@@ -0,0 +1,200 @@
+{
+    "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": 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
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "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": 2
+    },
+    "16": {
+        "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": 1
+    },
+    "24": {
+        "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
+    },
+    "32": {
+        "BLOCK_SIZE_M": 32,
+        "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
+    },
+    "48": {
+        "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
+    },
+    "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": 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
+    },
+    "128": {
+        "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
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 2
+    },
+    "512": {
+        "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": 2
+    },
+    "1024": {
+        "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": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "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": 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": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "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
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1024,device_name=NVIDIA_H100.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 256,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1344,device_name=NVIDIA_A100-SXM4-40GB.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1344,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1344,device_name=NVIDIA_H100_80GB_HBM3.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=14336,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=14336,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "4": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "128": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "9216": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=1792,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "9216": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
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+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "13312": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=2048,device_name=Device_4000.json

@@ -0,0 +1,258 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "50": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "70": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "80": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "160": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "800": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "900": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "5000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "10000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "20000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=2048,device_name=Device_4001.json

@@ -0,0 +1,186 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "15": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "25": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "50": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "70": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "100": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "800": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "900": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "1000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "2000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "5000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "10000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=2688,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=2688,device_name=NVIDIA_H100_80GB_HBM3.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=3072,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=3200,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8.json

@@ -0,0 +1,130 @@
+{
+    "3328": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "768": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1792": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2560": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2816": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3584": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3840": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1280": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2304": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=3584,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
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+    "BLOCK_SIZE_K": 256,
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+    "num_warps": 4,
+    "num_stages": 2
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+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
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+    "GROUP_SIZE_M": 1,
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+    "num_stages": 3
+  },
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+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
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+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
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+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=3584,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,218 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
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+    "GROUP_SIZE_M": 1,
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+    "num_stages": 5
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
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+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "5120": {
+    "BLOCK_SIZE_M": 128,
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+    "num_warps": 4,
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+  },
+  "9216": {
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+    "num_stages": 3
+  },
+  "13312": {
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+    "num_stages": 3
+  },
+  "17408": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
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+    "GROUP_SIZE_M": 16,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "25600": {
+    "BLOCK_SIZE_M": 128,
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+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "33792": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "41984": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "50176": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "58368": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=4096,device_name=Device_4000.json

@@ -0,0 +1,258 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "8": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "50": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "70": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "80": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "96": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "160": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "800": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "900": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "5000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "10000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "20000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=6400,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8.json

@@ -0,0 +1,130 @@
+{
+    "3840": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1792": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3584": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2816": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "1280": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "768": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3328": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2560": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2304": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
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+    "GROUP_SIZE_M": 1,
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+    "num_stages": 3
+  },
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+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
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+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
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+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
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+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 4
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_A100-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 5
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 2
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 64,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 256,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=7168,device_name=NVIDIA_H100_80GB_HBM3,dtype=int8_w8a16.json

@@ -0,0 +1,146 @@
+{
+  "1": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "2": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "4": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "8": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "16": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "24": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 32,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "32": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "48": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "64": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 16,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "96": {
+    "BLOCK_SIZE_M": 16,
+    "BLOCK_SIZE_N": 64,
+    "BLOCK_SIZE_K": 256,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 4,
+    "num_stages": 4
+  },
+  "128": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "256": {
+    "BLOCK_SIZE_M": 32,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 4,
+    "num_stages": 3
+  },
+  "512": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "1024": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 5
+  },
+  "1536": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "2048": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 64,
+    "num_warps": 8,
+    "num_stages": 3
+  },
+  "3072": {
+    "BLOCK_SIZE_M": 128,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 64,
+    "GROUP_SIZE_M": 32,
+    "num_warps": 8,
+    "num_stages": 4
+  },
+  "4096": {
+    "BLOCK_SIZE_M": 256,
+    "BLOCK_SIZE_N": 128,
+    "BLOCK_SIZE_K": 128,
+    "GROUP_SIZE_M": 1,
+    "num_warps": 8,
+    "num_stages": 3
+  }
+}

vllm/model_executor/layers/fused_moe/configs/E=16,N=800,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8.json

@@ -0,0 +1,130 @@
+{
+    "2048": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1792": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3328": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2560": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "768": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2816": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2304": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "1280": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "3840": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3584": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=160,N=192,device_name=Device_4000,dtype=int8_w8a8.json

@@ -0,0 +1,234 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "50": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "70": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "80": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "160": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "800": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "900": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1000": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=160,N=192,device_name=Device_4000.json

@@ -0,0 +1,226 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "50": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "70": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "80": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "160": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "800": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "900": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1000": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "2000": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=160,N=192,device_name=Device_4001.json

@@ -0,0 +1,242 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "50": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "70": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "80": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "160": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 32,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "800": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "900": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1000": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "2000": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "5000": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=160,N=192,device_name=NVIDIA_A800-SXM4-80GB.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=160,N=96,device_name=Device_4000.json

@@ -0,0 +1,234 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "10": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "16": {
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+    },
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+        "num_stages": 2
+    },
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+    },
+    "2048": {
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+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
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+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
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+        "num_stages": 2
+    },
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+        "BLOCK_SIZE_M": 128,
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+    },
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+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=160,N=96,device_name=Device_4001.json

@@ -0,0 +1,242 @@
+{
+    "1": {
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+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
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+    },
+    "2": {
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+    },
+    "4": {
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+        "GROUP_SIZE_M": 64,
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+    },
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+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "10": {
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+        "num_stages": 2
+    },
+    "16": {
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+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "20": {
+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
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+    },
+    "24": {
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+    },
+    "32": {
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+    },
+    "48": {
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+    "128": {
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+    },
+    "1536": {
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+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
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+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
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+        "num_stages": 2
+    },
+    "3600": {
+        "BLOCK_SIZE_M": 64,
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+        "GROUP_SIZE_M": 1,
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+        "num_stages": 2
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
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+        "GROUP_SIZE_M": 16,
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+        "num_stages": 2
+    },
+    "5000": {
+        "BLOCK_SIZE_M": 64,
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+        "GROUP_SIZE_M": 1,
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+        "num_stages": 2
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=1024,device_name=AMD_Instinct_MI325X,block_shape=[128,128].json

@@ -0,0 +1,200 @@
+{
+    "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": {
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+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
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+        "kpack": 2
+    },
+    "24": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
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+        "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,
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+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
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+        "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,
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+        "BLOCK_SIZE_K": 128,
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+        "num_warps": 8,
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+    },
+    "96": {
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+        "kpack": 2
+    },
+    "128": {
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+        "GROUP_SIZE_M": 32,
+        "num_warps": 1,
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+        "kpack": 2
+    },
+    "256": {
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+        "GROUP_SIZE_M": 32,
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+        "kpack": 1
+    },
+    "512": {
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+        "GROUP_SIZE_M": 8,
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+    "1024": {
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+        "GROUP_SIZE_M": 8,
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+    },
+    "1536": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
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+        "num_stages": 2,
+        "waves_per_eu": 0,
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+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 16,
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+        "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,
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+    },
+    "4096": {
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+        "GROUP_SIZE_M": 8,
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+        "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

@@ -0,0 +1,200 @@
+{
+    "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": {
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+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0,
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+        "kpack": 2
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
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+        "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,
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+        "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=128,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=128,device_name=NVIDIA_A100-SXM4-80GB,dtype=int8_w8a8.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=128,device_name=NVIDIA_A800-SXM4-80GB,dtype=int8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=128,device_name=NVIDIA_A800-SXM4-80GB,dtype=int8_w8a8.json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=128,device_name=NVIDIA_H100_80GB_HBM3,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
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+    },
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+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
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+    },
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+        "BLOCK_SIZE_M": 64,
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+        "num_stages": 3
+    },
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+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
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+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
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+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=128,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
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+        "BLOCK_SIZE_N": 64,
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+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
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+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=128,device_name=NVIDIA_L20Y,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=AMD_Instinct_MI300X,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,164 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "8": {
+        "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
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "512": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 2,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 4,
+        "num_stages": 2,
+        "waves_per_eu": 0
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=AMD_Instinct_MI325X,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,200 @@
+{
+    "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": {
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+        "BLOCK_SIZE_N": 32,
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+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 2
+    },
+    "256": {
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+        "GROUP_SIZE_M": 1,
+        "num_warps": 2,
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+        "kpack": 2
+    },
+    "512": {
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+        "kpack": 2
+    },
+    "1024": {
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+        "BLOCK_SIZE_K": 128,
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+        "kpack": 2
+    },
+    "1536": {
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+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2,
+        "waves_per_eu": 0,
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+        "kpack": 2
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 128,
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+        "GROUP_SIZE_M": 8,
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+        "num_stages": 2,
+        "waves_per_eu": 0,
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+        "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

@@ -0,0 +1,200 @@
+{
+    "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": {
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+        "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,
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+        "waves_per_eu": 0,
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+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
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+        "num_stages": 2,
+        "waves_per_eu": 0,
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+    },
+    "64": {
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+        "BLOCK_SIZE_N": 64,
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+        "GROUP_SIZE_M": 1,
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+    },
+    "96": {
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+    },
+    "128": {
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+        "GROUP_SIZE_M": 4,
+        "num_warps": 1,
+        "num_stages": 2,
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+        "kpack": 2
+    },
+    "256": {
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+        "GROUP_SIZE_M": 1,
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+    },
+    "512": {
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+        "GROUP_SIZE_M": 8,
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+    },
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 8,
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+        "kpack": 2
+    },
+    "1536": {
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+        "BLOCK_SIZE_N": 128,
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+        "GROUP_SIZE_M": 8,
+        "num_warps": 8,
+        "num_stages": 2,
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+        "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=Device_4000,dtype=int4_w4a16.json

@@ -0,0 +1,398 @@
+{
+    "1": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 64,
+            "num_warps": 8,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": "unroll;unprefetch;fullstage"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 64,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        }
+    },
+    "2": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "cpasync",
+            "scenario": "roll;;"
+        }
+    },
+    "4": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 16,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ";;fullstage"
+        }
+    },
+    "8": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "16": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "24": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 64,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "32": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 32,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 64,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "48": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 16,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "64": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 64,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "96": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 32,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "128": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 16,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "256": {
+        "stage1": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "512": {
+        "stage1": {
+            "BLOCK_SIZE_M": 32,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 32,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 32,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 16,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "1024": {
+        "stage1": {
+            "BLOCK_SIZE_M": 64,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ";;fullstage"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 64,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;"
+        }
+    },
+    "1536": {
+        "stage1": {
+            "BLOCK_SIZE_M": 64,
+            "BLOCK_SIZE_N": 32,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "cpasync",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 64,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;"
+        }
+    },
+    "2048": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 8,
+            "num_stages": 3,
+            "pipeline": "cpasync",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": "roll;;fullstage"
+        }
+    },
+    "3072": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 8,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "cpasync",
+            "scenario": ";;fullstage"
+        }
+    },
+    "4096": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 8,
+            "num_stages": 3,
+            "pipeline": "cpasync",
+            "scenario": ";;"
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": "roll;;"
+        }
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=Device_4000,dtype=int4_w8a16.json

@@ -0,0 +1,138 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 2
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 32,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
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+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=Device_4000,dtype=int8_w8a8.json

@@ -0,0 +1,398 @@
+{
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+        }
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+        }
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=Device_4000.json

@@ -0,0 +1,376 @@
+{
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+            "pipeline": "basic",
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+    },
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+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 128,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "cpasync",
+            "scenario": ""
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 16,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 32,
+            "GROUP_SIZE_M": 32,
+            "num_warps": 4,
+            "num_stages": 3,
+            "pipeline": "basic",
+            "scenario": ""
+        }
+    },
+    "512": {
+        "stage1": {
+            "BLOCK_SIZE_M": 32,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 8,
+            "num_stages": 4,
+            "pipeline": "basic",
+            "scenario": ""
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 32,
+            "BLOCK_SIZE_N": 64,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 32,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        }
+    },
+    "1024": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 128,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        }
+    },
+    "2048": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 128,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        }
+    },
+    "3072": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 128,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        }
+    },
+    "4096": {
+        "stage1": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 128,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        },
+        "stage2": {
+            "BLOCK_SIZE_M": 128,
+            "BLOCK_SIZE_N": 128,
+            "BLOCK_SIZE_K": 64,
+            "GROUP_SIZE_M": 1,
+            "num_warps": 4,
+            "num_stages": 4,
+            "pipeline": "cpasync",
+            "scenario": ""
+        }
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=NVIDIA_B200,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 5
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 32,
+        "BLOCK_SIZE_N": 256,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=NVIDIA_H20,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "64": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=NVIDIA_H20,dtype=int8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 128,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 64,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 16,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=NVIDIA_H20-3e,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 4
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "24": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "32": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "48": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "64": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "96": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "128": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "256": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "512": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "1536": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "3072": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4096": {
+        "BLOCK_SIZE_M": 64,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=NVIDIA_H200,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
+        "BLOCK_SIZE_M": 16,
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+    },
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+    },
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+    },
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+    },
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+        "num_stages": 3
+    },
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+    },
+    "1024": {
+        "BLOCK_SIZE_M": 64,
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+    },
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+    },
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+        "num_stages": 3
+    },
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+        "num_stages": 3
+    }
+}

vllm/model_executor/layers/fused_moe/configs/E=256,N=256,device_name=NVIDIA_L20,dtype=fp8_w8a8,block_shape=[128,128].json

@@ -0,0 +1,146 @@
+{
+    "1": {
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+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 8,
+        "num_stages": 3
+    },
+    "2": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
+        "num_stages": 3
+    },
+    "4": {
+        "BLOCK_SIZE_M": 16,
+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 64,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "8": {
+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 32,
+        "num_warps": 8,
+        "num_stages": 2
+    },
+    "16": {
+        "BLOCK_SIZE_M": 16,
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+    },
+    "24": {
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+        "GROUP_SIZE_M": 16,
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+        "num_stages": 2
+    },
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+        "BLOCK_SIZE_K": 256,
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+    },
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+        "num_stages": 2
+    },
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+        "GROUP_SIZE_M": 64,
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+    },
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+        "BLOCK_SIZE_M": 16,
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+    },
+    "256": {
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+        "BLOCK_SIZE_K": 256,
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+        "GROUP_SIZE_M": 1,
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+        "num_stages": 2
+    },
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+        "GROUP_SIZE_M": 1,
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+    },
+    "2048": {
+        "BLOCK_SIZE_M": 64,
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+    },
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+        "GROUP_SIZE_M": 16,
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+        "num_stages": 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

@@ -0,0 +1,200 @@
+{
+    "1": {
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+        "BLOCK_SIZE_N": 16,
+        "BLOCK_SIZE_K": 256,
+        "GROUP_SIZE_M": 1,
+        "num_warps": 4,
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+        "waves_per_eu": 0,
+        "matrix_instr_nonkdim": 16,
+        "kpack": 1
+    },
+    "2": {
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+        "num_stages": 2,
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+    },
+    "4": {
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+        "kpack": 1
+    },
+    "8": {
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+    },
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+        "GROUP_SIZE_M": 1,
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+        "kpack": 2
+    },
+    "32": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 1,
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+    },
+    "48": {
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+    },
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+        "BLOCK_SIZE_M": 16,
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
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+        "num_stages": 2,
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+        "kpack": 2
+    },
+    "128": {
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+        "BLOCK_SIZE_K": 128,
+        "GROUP_SIZE_M": 4,
+        "num_warps": 1,
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+    },
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+    },
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+        "GROUP_SIZE_M": 8,
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+        "BLOCK_SIZE_K": 128,
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+    },
+    "2048": {
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+        "BLOCK_SIZE_N": 128,
+        "BLOCK_SIZE_K": 64,
+        "GROUP_SIZE_M": 8,
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+        "kpack": 2
+    },
+    "3072": {
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+        "kpack": 2
+    },
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+        "GROUP_SIZE_M": 8,
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+        "kpack": 2
+    }
+}