support Hy3 preview (#23533)
Co-authored-by: pengmeng <pengmeng@tencent.com> Co-authored-by: Qiaolin-Yu <liin1211@outlook.com> Co-authored-by: chengvjiang <chengvjiang@tencent.com> Co-authored-by: russellfeng <russellfeng@tencent.com>
This commit is contained in:
Xinyuan Tong
co-authored by
pengmeng
Qiaolin-Yu
chengvjiang
russellfeng
pengmeng
Qiaolin-Yu
chengvjiang
russellfeng
parent
6344b546c8
commit
6d03861476
@@ -0,0 +1,267 @@
|
||||
/*
|
||||
* Fused grouped top-k kernel for MoE routing.
|
||||
* Adapted from vLLM's grouped_topk_kernels.cu (Apache-2.0).
|
||||
*
|
||||
* Handles single-group (num_expert_group=1) and multi-group cases with
|
||||
* sigmoid scoring, bias correction, renormalization and scaling factor.
|
||||
* Supports up to 512 experts and topk up to 8.
|
||||
*/
|
||||
#include <sgl_kernel/tensor.h> // For TensorMatcher, SymbolicSize, SymbolicDevice
|
||||
#include <sgl_kernel/utils.h> // For RuntimeCheck, div_ceil
|
||||
|
||||
#include <sgl_kernel/utils.cuh> // For LaunchKernel, fp32_t
|
||||
|
||||
#include <dlpack/dlpack.h>
|
||||
#include <tvm/ffi/container/tensor.h>
|
||||
|
||||
#include <cfloat>
|
||||
#include <cstdint>
|
||||
|
||||
namespace {
|
||||
|
||||
static constexpr int WARP_SIZE = 32;
|
||||
static constexpr int MAX_TOPK = 8;
|
||||
|
||||
// Pack (value, index) into a single uint64_t for warp-level max reduction.
|
||||
// Uses IEEE 754 bit-trick: float bits are order-preserving for positive values.
|
||||
// Since sigmoid + positive bias yields non-negative scores, this works correctly.
|
||||
__device__ __forceinline__ uint64_t pack_val_idx(float val, int32_t idx) {
|
||||
uint32_t val_bits = __float_as_uint(val);
|
||||
// Flip sign bit so that comparison works for all floats
|
||||
val_bits ^= ((val_bits >> 31) | 0x80000000u);
|
||||
// Use (65535 - idx) so that smaller indices win ties
|
||||
uint32_t idx_bits = static_cast<uint32_t>(65535 - idx);
|
||||
return (static_cast<uint64_t>(val_bits) << 32) | idx_bits;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ void unpack_val_idx(uint64_t packed, float& val, int32_t& idx) {
|
||||
uint32_t idx_bits = static_cast<uint32_t>(packed & 0xFFFFFFFF);
|
||||
idx = static_cast<int32_t>(65535 - idx_bits);
|
||||
uint32_t val_bits = static_cast<uint32_t>(packed >> 32);
|
||||
// Undo the sign-bit flip
|
||||
val_bits ^= (~(val_bits >> 31) | 0x80000000u);
|
||||
val = __uint_as_float(val_bits);
|
||||
}
|
||||
|
||||
__device__ __forceinline__ uint64_t warp_max_u64(uint64_t val) {
|
||||
#pragma unroll
|
||||
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
|
||||
uint64_t other = __shfl_xor_sync(0xffffffff, val, mask);
|
||||
val = max(val, other);
|
||||
}
|
||||
return val;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ float warp_sum_f32(float val) {
|
||||
#pragma unroll
|
||||
for (int mask = WARP_SIZE / 2; mask > 0; mask >>= 1) {
|
||||
val += __shfl_xor_sync(0xffffffff, val, mask);
|
||||
}
|
||||
return val;
|
||||
}
|
||||
|
||||
__device__ __forceinline__ float fast_sigmoid(float x) {
|
||||
return 1.0f / (1.0f + __expf(-x));
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
// Kernel: one block per token, MaxExperts threads per block.
|
||||
// Each thread handles one expert (or is idle if threadIdx.x >= numExperts).
|
||||
//
|
||||
// Phase 1: All threads load score → sigmoid → +bias → shared memory.
|
||||
// Phase 2: Warp 0 iteratively selects top-k via packed warp-level max reduce.
|
||||
// Phase 3: Warp 0 renormalizes and writes output.
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
template <int MaxExperts>
|
||||
__global__ void grouped_topk_single_group_kernel(
|
||||
const float* __restrict__ scores,
|
||||
float* __restrict__ topk_values,
|
||||
int32_t* __restrict__ topk_indices,
|
||||
const float* __restrict__ bias,
|
||||
int64_t num_tokens,
|
||||
int64_t num_experts,
|
||||
int64_t topk,
|
||||
bool renormalize,
|
||||
float scaling_factor) {
|
||||
__shared__ float smem_sigmoid[MaxExperts];
|
||||
__shared__ float smem_biased[MaxExperts];
|
||||
|
||||
int64_t token_id = blockIdx.x;
|
||||
if (token_id >= num_tokens) return;
|
||||
|
||||
int tid = threadIdx.x;
|
||||
const float* token_scores = scores + token_id * num_experts;
|
||||
|
||||
// Phase 1: load → sigmoid → bias → shared memory
|
||||
float score_sig = -FLT_MAX;
|
||||
float score_biased = -FLT_MAX;
|
||||
if (tid < num_experts) {
|
||||
float raw = token_scores[tid];
|
||||
score_sig = fast_sigmoid(raw);
|
||||
score_biased = score_sig + bias[tid];
|
||||
}
|
||||
smem_sigmoid[tid] = score_sig;
|
||||
smem_biased[tid] = score_biased;
|
||||
__syncthreads();
|
||||
|
||||
// Phase 2 & 3: warp 0 selects top-k
|
||||
int warp_id = tid / WARP_SIZE;
|
||||
int lane_id = tid % WARP_SIZE;
|
||||
|
||||
if (warp_id != 0) return;
|
||||
|
||||
float* out_vals = topk_values + token_id * topk;
|
||||
int32_t* out_ids = topk_indices + token_id * topk;
|
||||
|
||||
// Each lane scans ceil(num_experts/32) experts per iteration
|
||||
float selected_weights[MAX_TOPK];
|
||||
int32_t selected_ids[MAX_TOPK];
|
||||
|
||||
for (int k = 0; k < topk; k++) {
|
||||
// Each lane finds its local max among its assigned experts
|
||||
float my_max_val = -FLT_MAX;
|
||||
int32_t my_max_idx = 0;
|
||||
for (int i = lane_id; i < num_experts; i += WARP_SIZE) {
|
||||
float v = smem_biased[i];
|
||||
if (v > my_max_val) {
|
||||
my_max_val = v;
|
||||
my_max_idx = i;
|
||||
}
|
||||
}
|
||||
|
||||
// Warp-level max reduction using packed value+index
|
||||
uint64_t packed = pack_val_idx(my_max_val, my_max_idx);
|
||||
uint64_t best = warp_max_u64(packed);
|
||||
|
||||
float best_val;
|
||||
int32_t best_idx;
|
||||
unpack_val_idx(best, best_val, best_idx);
|
||||
|
||||
selected_ids[k] = best_idx;
|
||||
selected_weights[k] = smem_sigmoid[best_idx];
|
||||
|
||||
// Mark selected expert so it won't be picked again
|
||||
if (lane_id == best_idx % WARP_SIZE && (best_idx / WARP_SIZE) == 0) {
|
||||
smem_biased[best_idx] = -FLT_MAX;
|
||||
}
|
||||
// Handle indices >= 32: the owning lane must clear it
|
||||
if (best_idx >= WARP_SIZE) {
|
||||
if (lane_id == 0) {
|
||||
smem_biased[best_idx] = -FLT_MAX;
|
||||
}
|
||||
} else {
|
||||
if (lane_id == best_idx) {
|
||||
smem_biased[best_idx] = -FLT_MAX;
|
||||
}
|
||||
}
|
||||
__syncwarp();
|
||||
}
|
||||
|
||||
// Phase 3: renormalize and write output
|
||||
if (lane_id < topk) {
|
||||
float weight = selected_weights[lane_id];
|
||||
float final_weight = weight * scaling_factor;
|
||||
|
||||
if (renormalize) {
|
||||
// Warp-level sum of selected weights (only lanes < topk contribute)
|
||||
float partial = (lane_id < topk) ? weight : 0.0f;
|
||||
float total = warp_sum_f32(partial);
|
||||
final_weight = weight * scaling_factor / (total + 1e-20f);
|
||||
}
|
||||
|
||||
out_ids[lane_id] = selected_ids[lane_id];
|
||||
out_vals[lane_id] = final_weight;
|
||||
}
|
||||
}
|
||||
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
// Launcher
|
||||
// ─────────────────────────────────────────────────────────────────────────────
|
||||
void grouped_topk(
|
||||
tvm::ffi::TensorView scores,
|
||||
tvm::ffi::TensorView bias,
|
||||
tvm::ffi::TensorView topk_values,
|
||||
tvm::ffi::TensorView topk_indices,
|
||||
int64_t num_expert_group,
|
||||
int64_t topk_group,
|
||||
int64_t topk,
|
||||
bool renormalize,
|
||||
double scaling_factor) {
|
||||
using namespace host;
|
||||
|
||||
SymbolicSize N{"num_tokens"};
|
||||
SymbolicSize E{"num_experts"};
|
||||
SymbolicDevice device_;
|
||||
device_.set_options<kDLCUDA>();
|
||||
|
||||
TensorMatcher({N, E}).with_dtype<fp32_t>().with_device<kDLCUDA>(device_).verify(scores);
|
||||
|
||||
TensorMatcher({E}).with_dtype<fp32_t>().with_device<kDLCUDA>(device_).verify(bias);
|
||||
|
||||
SymbolicSize K{"topk"};
|
||||
TensorMatcher({N, K}).with_dtype<fp32_t>().with_device<kDLCUDA>(device_).verify(topk_values);
|
||||
|
||||
TensorMatcher({N, K}).with_dtype<int32_t>().with_device<kDLCUDA>(device_).verify(topk_indices);
|
||||
|
||||
int64_t num_tokens = N.unwrap();
|
||||
int64_t num_experts = E.unwrap();
|
||||
DLDevice device = device_.unwrap();
|
||||
|
||||
RuntimeCheck(num_expert_group == 1 && topk_group == 1, "This kernel only supports num_expert_group=1, topk_group=1");
|
||||
RuntimeCheck(topk <= MAX_TOPK, "topk must be <= ", MAX_TOPK);
|
||||
RuntimeCheck(num_experts <= 512, "num_experts must be <= 512");
|
||||
|
||||
if (num_tokens == 0) return;
|
||||
|
||||
float scale_f = static_cast<float>(scaling_factor);
|
||||
|
||||
auto* score_ptr = static_cast<const float*>(scores.data_ptr());
|
||||
auto* bias_ptr = static_cast<const float*>(bias.data_ptr());
|
||||
auto* val_ptr = static_cast<float*>(topk_values.data_ptr());
|
||||
auto* idx_ptr = static_cast<int32_t*>(topk_indices.data_ptr());
|
||||
|
||||
// Select template based on expert count (round up to next tier)
|
||||
int num_threads;
|
||||
if (num_experts <= 128) {
|
||||
num_threads = 128;
|
||||
LaunchKernel(static_cast<uint32_t>(num_tokens), num_threads, device)(
|
||||
grouped_topk_single_group_kernel<128>,
|
||||
score_ptr,
|
||||
val_ptr,
|
||||
idx_ptr,
|
||||
bias_ptr,
|
||||
num_tokens,
|
||||
num_experts,
|
||||
topk,
|
||||
renormalize,
|
||||
scale_f);
|
||||
} else if (num_experts <= 256) {
|
||||
num_threads = 256;
|
||||
LaunchKernel(static_cast<uint32_t>(num_tokens), num_threads, device)(
|
||||
grouped_topk_single_group_kernel<256>,
|
||||
score_ptr,
|
||||
val_ptr,
|
||||
idx_ptr,
|
||||
bias_ptr,
|
||||
num_tokens,
|
||||
num_experts,
|
||||
topk,
|
||||
renormalize,
|
||||
scale_f);
|
||||
} else {
|
||||
num_threads = 512;
|
||||
LaunchKernel(static_cast<uint32_t>(num_tokens), num_threads, device)(
|
||||
grouped_topk_single_group_kernel<512>,
|
||||
score_ptr,
|
||||
val_ptr,
|
||||
idx_ptr,
|
||||
bias_ptr,
|
||||
num_tokens,
|
||||
num_experts,
|
||||
topk,
|
||||
renormalize,
|
||||
scale_f);
|
||||
}
|
||||
}
|
||||
|
||||
} // namespace
|
||||
@@ -0,0 +1,89 @@
|
||||
"""Fused grouped top-k kernel for MoE routing (single-group, sigmoid scoring)."""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import TYPE_CHECKING, Tuple
|
||||
|
||||
import torch
|
||||
|
||||
from sglang.jit_kernel.utils import cache_once, load_jit
|
||||
from sglang.srt.utils.custom_op import register_custom_op
|
||||
|
||||
if TYPE_CHECKING:
|
||||
from tvm_ffi.module import Module
|
||||
|
||||
|
||||
@cache_once
|
||||
def _jit_grouped_topk_module() -> Module:
|
||||
return load_jit(
|
||||
"grouped_topk",
|
||||
cuda_files=["moe/grouped_topk.cuh"],
|
||||
cuda_wrappers=[("grouped_topk", "grouped_topk")],
|
||||
)
|
||||
|
||||
|
||||
@register_custom_op(mutates_args=["topk_values", "topk_indices"])
|
||||
def _jit_grouped_topk_op(
|
||||
scores: torch.Tensor,
|
||||
bias: torch.Tensor,
|
||||
topk_values: torch.Tensor,
|
||||
topk_indices: torch.Tensor,
|
||||
num_expert_group: int,
|
||||
topk_group: int,
|
||||
topk: int,
|
||||
renormalize: bool,
|
||||
scaling_factor: float,
|
||||
) -> None:
|
||||
module = _jit_grouped_topk_module()
|
||||
module.grouped_topk(
|
||||
scores,
|
||||
bias,
|
||||
topk_values,
|
||||
topk_indices,
|
||||
num_expert_group,
|
||||
topk_group,
|
||||
topk,
|
||||
renormalize,
|
||||
scaling_factor,
|
||||
)
|
||||
|
||||
|
||||
def grouped_topk(
|
||||
scores: torch.Tensor,
|
||||
bias: torch.Tensor,
|
||||
num_expert_group: int,
|
||||
topk_group: int,
|
||||
topk: int,
|
||||
renormalize: bool,
|
||||
scaling_factor: float,
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Fused sigmoid + bias + top-k + renormalize for MoE routing.
|
||||
|
||||
Replaces the naive PyTorch path that uses 3x torch.topk + scatter + masked_fill.
|
||||
Currently supports num_expert_group=1, topk_group=1, num_experts<=512, topk<=8.
|
||||
"""
|
||||
num_tokens = scores.shape[0]
|
||||
|
||||
topk_values = torch.empty(
|
||||
(num_tokens, topk), dtype=torch.float32, device=scores.device
|
||||
)
|
||||
topk_indices = torch.empty(
|
||||
(num_tokens, topk), dtype=torch.int32, device=scores.device
|
||||
)
|
||||
|
||||
if num_tokens == 0:
|
||||
return topk_values, topk_indices
|
||||
|
||||
_jit_grouped_topk_op(
|
||||
scores.contiguous(),
|
||||
bias.contiguous(),
|
||||
topk_values,
|
||||
topk_indices,
|
||||
num_expert_group,
|
||||
topk_group,
|
||||
topk,
|
||||
renormalize,
|
||||
scaling_factor,
|
||||
)
|
||||
return topk_values, topk_indices
|
||||
@@ -366,6 +366,10 @@ class ModelConfig:
|
||||
self.hf_config.architectures[0] = "NemotronHForCausalLMMTP"
|
||||
self.hf_config.num_nextn_predict_layers = 1
|
||||
|
||||
if is_draft_model and self.hf_config.architectures[0] == "HYV3ForCausalLM":
|
||||
self.hf_config.architectures[0] = "HYV3ForCausalLMNextN"
|
||||
self.hf_config.num_nextn_predict_layers = 1
|
||||
|
||||
def _derive_hybrid_model(self):
|
||||
# Use self.context_len after it has been initialized to prevent using context_len which may be None.
|
||||
self.is_hybrid_swa = (
|
||||
|
||||
@@ -1404,9 +1404,14 @@ class OpenAIServingChat(OpenAIServingBase):
|
||||
request.chat_template_kwargs is not None
|
||||
and request.chat_template_kwargs.get("enable_thinking") is True
|
||||
)
|
||||
if self.reasoning_parser in ["mistral"]:
|
||||
# Mistral models only reason when reasoning_effort is explicitly
|
||||
# set to a value other than None/"none" (typically "high").
|
||||
if self.reasoning_parser == "hunyuan":
|
||||
# Hy3-preview template emits no <think> when reasoning_effort is
|
||||
# "no_think" / "none" / unset; forcing reasoning would route all
|
||||
# output into reasoning_content.
|
||||
return request.reasoning_effort not in (None, "none", "no_think")
|
||||
if self.reasoning_parser == "mistral":
|
||||
# Mistral only reasons when reasoning_effort is explicitly set
|
||||
# to a non-"none" value (typically "high").
|
||||
return (
|
||||
request.reasoning_effort is not None
|
||||
and request.reasoning_effort != "none"
|
||||
|
||||
@@ -21,6 +21,7 @@ from sglang.srt.function_call.glm4_moe_detector import Glm4MoeDetector
|
||||
from sglang.srt.function_call.glm47_moe_detector import Glm47MoeDetector
|
||||
from sglang.srt.function_call.gpt_oss_detector import GptOssDetector
|
||||
from sglang.srt.function_call.hermes_detector import HermesDetector
|
||||
from sglang.srt.function_call.hunyuan_detector import HunyuanDetector
|
||||
from sglang.srt.function_call.internlm_detector import InternlmDetector
|
||||
from sglang.srt.function_call.kimik2_detector import KimiK2Detector
|
||||
from sglang.srt.function_call.lfm2_detector import Lfm2Detector
|
||||
@@ -73,6 +74,7 @@ class FunctionCallParser:
|
||||
"trinity": TrinityDetector,
|
||||
"interns1": InternlmDetector,
|
||||
"hermes": HermesDetector,
|
||||
"hunyuan": HunyuanDetector,
|
||||
"gigachat3": GigaChat3Detector,
|
||||
"gemma4": Gemma4Detector,
|
||||
}
|
||||
|
||||
@@ -0,0 +1,476 @@
|
||||
import json
|
||||
import logging
|
||||
import re
|
||||
from typing import Any, Dict, List, Optional, Set
|
||||
|
||||
from sglang.srt.entrypoints.openai.protocol import Tool
|
||||
from sglang.srt.environ import envs
|
||||
from sglang.srt.function_call.base_format_detector import BaseFormatDetector
|
||||
from sglang.srt.function_call.core_types import (
|
||||
StreamingParseResult,
|
||||
StructureInfo,
|
||||
ToolCallItem,
|
||||
_GetInfoFunc,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class HunyuanDetector(BaseFormatDetector):
|
||||
"""
|
||||
Detector for Hunyuan (HYV3) tool call format.
|
||||
|
||||
Format:
|
||||
<tool_calls>
|
||||
<tool_call>function_name<tool_sep>
|
||||
<arg_key>key1</arg_key>
|
||||
<arg_value>value1</arg_value>
|
||||
</tool_call>
|
||||
</tool_calls>
|
||||
|
||||
Streaming behavior:
|
||||
* Phase 1 emits the tool name once <tool_sep> is seen.
|
||||
* Phase 2 streams argument JSON incrementally. Closed <arg_value>
|
||||
pairs are parsed with schema-aware type coercion; pure-string
|
||||
args may be streamed char-by-char (with JSON escaping). The
|
||||
closing "}" is withheld until </tool_call> arrives.
|
||||
"""
|
||||
|
||||
_TYPE_ALIASES: Dict[str, str] = {
|
||||
"str": "string",
|
||||
"text": "string",
|
||||
"varchar": "string",
|
||||
"char": "string",
|
||||
"enum": "string",
|
||||
"bool": "boolean",
|
||||
"binary": "boolean",
|
||||
"int": "integer",
|
||||
"float": "number",
|
||||
"double": "number",
|
||||
"list": "array",
|
||||
"dict": "object",
|
||||
"map": "object",
|
||||
}
|
||||
|
||||
_INTEGER_PREFIXES = ("int", "uint", "long", "short", "unsigned")
|
||||
_NUMBER_PREFIXES = ("num", "float")
|
||||
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
self.bot_token = "<tool_calls>"
|
||||
self.eot_token = "</tool_calls>"
|
||||
|
||||
self.tool_call_start_token = "<tool_call>"
|
||||
self.tool_call_end_token = "</tool_call>"
|
||||
self.tool_sep_token = "<tool_sep>"
|
||||
|
||||
self.arg_key_start_token = "<arg_key>"
|
||||
self.arg_key_end_token = "</arg_key>"
|
||||
self.arg_value_start_token = "<arg_value>"
|
||||
self.arg_value_end_token = "</arg_value>"
|
||||
|
||||
self.tool_call_regex = re.compile(
|
||||
r"<tool_call>(.*?)<tool_sep>(.*?)</tool_call>", re.DOTALL
|
||||
)
|
||||
self.func_args_regex = re.compile(
|
||||
r"<arg_key>(.*?)</arg_key>\s*<arg_value>(.*?)</arg_value>", re.DOTALL
|
||||
)
|
||||
|
||||
# Streaming state
|
||||
self._in_tool_calls: bool = False
|
||||
self._streaming_tool_name: Optional[str] = None
|
||||
self._completed_args: Dict[str, Any] = {}
|
||||
self._streamed_json_len: int = 0
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Type-normalization helpers
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
@staticmethod
|
||||
def _normalize_type(raw_type: str) -> str:
|
||||
exact = HunyuanDetector._TYPE_ALIASES.get(raw_type)
|
||||
if exact is not None:
|
||||
return exact
|
||||
lower = raw_type.lower()
|
||||
if any(lower.startswith(p) for p in HunyuanDetector._INTEGER_PREFIXES):
|
||||
return "integer"
|
||||
if any(lower.startswith(p) for p in HunyuanDetector._NUMBER_PREFIXES):
|
||||
return "number"
|
||||
return raw_type
|
||||
|
||||
@staticmethod
|
||||
def _get_arg_schema(
|
||||
function_name: str, arg_key: str, tools: Optional[List[Tool]]
|
||||
) -> dict:
|
||||
if not tools:
|
||||
return {}
|
||||
for tool in tools:
|
||||
if tool.function.name == function_name:
|
||||
if tool.function.parameters is None:
|
||||
return {}
|
||||
return tool.function.parameters.get("properties", {}).get(arg_key, {})
|
||||
return {}
|
||||
|
||||
@staticmethod
|
||||
def _get_schema_options(arg_schema: dict) -> List[dict]:
|
||||
"""Priority: single ``type`` > ``anyOf`` > ``oneOf``; else default string."""
|
||||
if "type" in arg_schema:
|
||||
return [arg_schema]
|
||||
if "anyOf" in arg_schema:
|
||||
return arg_schema["anyOf"]
|
||||
if "oneOf" in arg_schema:
|
||||
return arg_schema["oneOf"]
|
||||
return [{"type": "string"}]
|
||||
|
||||
@staticmethod
|
||||
def _get_types(arg_schema: dict) -> Set[str]:
|
||||
schemas = HunyuanDetector._get_schema_options(arg_schema)
|
||||
return {
|
||||
HunyuanDetector._normalize_type(s.get("type", "string")) for s in schemas
|
||||
} - {"null"}
|
||||
|
||||
@staticmethod
|
||||
def _is_only_string_type(
|
||||
function_name: str, arg_key: str, tools: Optional[List[Tool]]
|
||||
) -> bool:
|
||||
"""Only pure-string args get char-by-char value streaming; compound
|
||||
types like anyOf(string | array) might resolve to a JSON array or
|
||||
object, so we can't safely stream them as open JSON strings."""
|
||||
arg_schema = HunyuanDetector._get_arg_schema(function_name, arg_key, tools)
|
||||
return HunyuanDetector._get_types(arg_schema) == {"string"}
|
||||
|
||||
@staticmethod
|
||||
def _try_parse_bool(value: str) -> Optional[bool]:
|
||||
lower = value.lower()
|
||||
if lower == "true":
|
||||
return True
|
||||
if lower == "false":
|
||||
return False
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _try_parse_int(value: str) -> Optional[int]:
|
||||
try:
|
||||
return int(value)
|
||||
except (ValueError, TypeError):
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _try_parse_number(value: str):
|
||||
"""int if no '.'/'e'/'E', else float."""
|
||||
try:
|
||||
if "." in value or "e" in value or "E" in value:
|
||||
return float(value)
|
||||
return int(value)
|
||||
except (ValueError, TypeError):
|
||||
return None
|
||||
|
||||
@staticmethod
|
||||
def _deserialize(value: str) -> Any:
|
||||
try:
|
||||
return json.loads(value)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
return value
|
||||
|
||||
@staticmethod
|
||||
def _parse_value(
|
||||
value: str,
|
||||
function_name: str,
|
||||
arg_key: str,
|
||||
tools: Optional[List[Tool]],
|
||||
) -> Any:
|
||||
"""Unified value parser: bool → int → number → json (array/obj) → string."""
|
||||
arg_schema = HunyuanDetector._get_arg_schema(function_name, arg_key, tools)
|
||||
types = HunyuanDetector._get_types(arg_schema)
|
||||
|
||||
if "boolean" in types:
|
||||
r = HunyuanDetector._try_parse_bool(value)
|
||||
if r is not None:
|
||||
return r
|
||||
|
||||
if "integer" in types:
|
||||
r = HunyuanDetector._try_parse_int(value)
|
||||
if r is not None:
|
||||
return r
|
||||
|
||||
if "number" in types:
|
||||
r = HunyuanDetector._try_parse_number(value)
|
||||
if r is not None:
|
||||
return r
|
||||
|
||||
if types - {"string", "boolean", "integer", "number"}:
|
||||
try:
|
||||
return json.loads(value)
|
||||
except (json.JSONDecodeError, ValueError):
|
||||
pass
|
||||
|
||||
if "string" in types:
|
||||
return value
|
||||
|
||||
return HunyuanDetector._deserialize(value)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Non-streaming
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def has_tool_call(self, text: str) -> bool:
|
||||
return self.bot_token in text
|
||||
|
||||
def detect_and_parse(self, text: str, tools: List[Tool]) -> StreamingParseResult:
|
||||
if self.bot_token not in text:
|
||||
return StreamingParseResult(normal_text=text, calls=[])
|
||||
|
||||
idx = text.find(self.bot_token)
|
||||
normal_text = text[:idx].strip() if idx > 0 else ""
|
||||
|
||||
tool_indices = self._get_tool_indices(tools)
|
||||
forward_unknown = envs.SGLANG_FORWARD_UNKNOWN_TOOLS.get()
|
||||
|
||||
calls: List[ToolCallItem] = []
|
||||
try:
|
||||
for function_name, function_args in self.tool_call_regex.findall(text):
|
||||
function_name = function_name.strip()
|
||||
if function_name not in tool_indices and not forward_unknown:
|
||||
logger.warning(
|
||||
"Model attempted to call undefined function: %s", function_name
|
||||
)
|
||||
continue
|
||||
|
||||
arg_dict: Dict[str, Any] = {}
|
||||
for key, value in self.func_args_regex.findall(function_args):
|
||||
key = key.strip()
|
||||
arg_dict[key] = self._parse_value(value, function_name, key, tools)
|
||||
|
||||
calls.append(
|
||||
ToolCallItem(
|
||||
tool_index=tool_indices.get(function_name, -1),
|
||||
name=function_name,
|
||||
parameters=json.dumps(arg_dict, ensure_ascii=False),
|
||||
)
|
||||
)
|
||||
return StreamingParseResult(normal_text=normal_text, calls=calls)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in detect_and_parse: {e}", exc_info=True)
|
||||
return StreamingParseResult(normal_text=text)
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# Streaming
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _reset_streaming_tool_state(self):
|
||||
self._streaming_tool_name = None
|
||||
self._completed_args = {}
|
||||
self._streamed_json_len = 0
|
||||
|
||||
def parse_streaming_increment(
|
||||
self, new_text: str, tools: List[Tool]
|
||||
) -> StreamingParseResult:
|
||||
try:
|
||||
return self._parse_streaming_increment_impl(new_text, tools)
|
||||
except Exception as e:
|
||||
logger.error(f"Error in parse_streaming_increment: {e}", exc_info=True)
|
||||
return StreamingParseResult()
|
||||
|
||||
def _parse_streaming_increment_impl(
|
||||
self, new_text: str, tools: List[Tool]
|
||||
) -> StreamingParseResult:
|
||||
if not hasattr(self, "_tool_indices"):
|
||||
self._tool_indices = self._get_tool_indices(tools)
|
||||
|
||||
# Not yet inside <tool_calls>: emit normal text or buffer partial bot_token.
|
||||
if not self._in_tool_calls:
|
||||
combined = self._buffer + new_text
|
||||
if self.bot_token in combined:
|
||||
bot_pos = combined.find(self.bot_token)
|
||||
normal_text = combined[:bot_pos]
|
||||
self._buffer = combined[bot_pos + len(self.bot_token) :]
|
||||
self._in_tool_calls = True
|
||||
return self._continue_streaming(tools, leading_normal=normal_text)
|
||||
|
||||
partial_len = self._ends_with_partial_token(combined, self.bot_token)
|
||||
if partial_len:
|
||||
self._buffer = combined[-partial_len:]
|
||||
return StreamingParseResult(normal_text=combined[:-partial_len])
|
||||
self._buffer = ""
|
||||
return StreamingParseResult(normal_text=combined)
|
||||
|
||||
self._buffer += new_text
|
||||
return self._continue_streaming(tools)
|
||||
|
||||
def _continue_streaming(
|
||||
self, tools: List[Tool], leading_normal: str = ""
|
||||
) -> StreamingParseResult:
|
||||
"""Drive the state machine after <tool_calls> is open."""
|
||||
calls: List[ToolCallItem] = []
|
||||
|
||||
while True:
|
||||
if self._streaming_tool_name is None:
|
||||
# Phase 1: wait for <tool_call>..<tool_sep>.
|
||||
tc_start = self._buffer.find(self.tool_call_start_token)
|
||||
if tc_start == -1:
|
||||
if self.eot_token in self._buffer:
|
||||
eot_pos = self._buffer.find(self.eot_token)
|
||||
self._buffer = self._buffer[eot_pos + len(self.eot_token) :]
|
||||
self._in_tool_calls = False
|
||||
break
|
||||
|
||||
sep_pos = self._buffer.find(self.tool_sep_token, tc_start)
|
||||
if sep_pos == -1:
|
||||
self._buffer = self._buffer[tc_start:]
|
||||
break
|
||||
|
||||
tool_name = self._buffer[
|
||||
tc_start + len(self.tool_call_start_token) : sep_pos
|
||||
].strip()
|
||||
|
||||
if (
|
||||
tool_name not in self._tool_indices
|
||||
and not envs.SGLANG_FORWARD_UNKNOWN_TOOLS.get()
|
||||
):
|
||||
logger.warning(
|
||||
"Model attempted to call undefined function: %s", tool_name
|
||||
)
|
||||
|
||||
self._streaming_tool_name = tool_name
|
||||
self.current_tool_id += 1
|
||||
while len(self.streamed_args_for_tool) <= self.current_tool_id:
|
||||
self.streamed_args_for_tool.append("")
|
||||
|
||||
calls.append(
|
||||
ToolCallItem(
|
||||
tool_index=self.current_tool_id,
|
||||
name=tool_name,
|
||||
parameters="",
|
||||
)
|
||||
)
|
||||
|
||||
self._buffer = self._buffer[sep_pos + len(self.tool_sep_token) :]
|
||||
|
||||
# Phase 2: stream argument JSON of the current tool.
|
||||
before_name = self._streaming_tool_name
|
||||
calls.extend(self._stream_args(tools))
|
||||
if self._streaming_tool_name is not None:
|
||||
break # current tool still open; need more data.
|
||||
if self._streaming_tool_name == before_name:
|
||||
break # safety: avoid infinite loop if state didn't advance.
|
||||
|
||||
return StreamingParseResult(normal_text=leading_normal, calls=calls)
|
||||
|
||||
def _stream_args(self, tools: List[Tool]) -> List[ToolCallItem]:
|
||||
"""Emit argument-JSON deltas for the currently-open tool call."""
|
||||
is_complete = self.tool_call_end_token in self._buffer
|
||||
|
||||
if is_complete:
|
||||
end_idx = self._buffer.find(self.tool_call_end_token)
|
||||
args_text = self._buffer[:end_idx]
|
||||
else:
|
||||
args_text = self._buffer
|
||||
|
||||
# 1. Absorb closed <arg_key>..<arg_value> pairs.
|
||||
last_closed_end = 0
|
||||
for m in self.func_args_regex.finditer(args_text):
|
||||
key, value = m.groups()
|
||||
key = key.strip()
|
||||
if key not in self._completed_args:
|
||||
self._completed_args[key] = self._parse_value(
|
||||
value, self._streaming_tool_name or "", key, tools
|
||||
)
|
||||
last_closed_end = m.end()
|
||||
|
||||
# 2. Detect a partial (unclosed) kv pair at the tail.
|
||||
tail = args_text[last_closed_end:]
|
||||
partial_key: Optional[str] = None
|
||||
partial_value: Optional[str] = None
|
||||
|
||||
ak_start = tail.find(self.arg_key_start_token)
|
||||
if ak_start != -1:
|
||||
ak_end = tail.find(
|
||||
self.arg_key_end_token, ak_start + len(self.arg_key_start_token)
|
||||
)
|
||||
if ak_end != -1:
|
||||
partial_key = tail[
|
||||
ak_start + len(self.arg_key_start_token) : ak_end
|
||||
].strip()
|
||||
av_start = tail.find(self.arg_value_start_token, ak_end)
|
||||
if av_start != -1 and self._is_only_string_type(
|
||||
self._streaming_tool_name or "", partial_key, tools
|
||||
):
|
||||
partial_value = tail[av_start + len(self.arg_value_start_token) :]
|
||||
|
||||
# Avoid emitting a lone "{" before any arg content is knowable.
|
||||
if not is_complete and not self._completed_args and partial_value is None:
|
||||
return []
|
||||
|
||||
# 3. Build the JSON snapshot manually to control streaming boundaries.
|
||||
snapshot_parts: List[str] = []
|
||||
for k, v in self._completed_args.items():
|
||||
k_json = json.dumps(k, ensure_ascii=False)
|
||||
v_json = json.dumps(v, ensure_ascii=False)
|
||||
snapshot_parts.append(f"{k_json}: {v_json}")
|
||||
|
||||
if partial_key is not None and partial_value is not None:
|
||||
# Hold back chars that could be a partial </arg_value> marker so
|
||||
# that a `<` starting the end-tag doesn't leak into the streamed
|
||||
# JSON string value.
|
||||
hold = self._ends_with_partial_token(
|
||||
partial_value, self.arg_value_end_token
|
||||
)
|
||||
safe_value = partial_value[:-hold] if hold else partial_value
|
||||
k_json = json.dumps(partial_key, ensure_ascii=False)
|
||||
escaped = (
|
||||
safe_value.replace("\\", "\\\\")
|
||||
.replace('"', '\\"')
|
||||
.replace("\n", "\\n")
|
||||
.replace("\r", "\\r")
|
||||
.replace("\t", "\\t")
|
||||
)
|
||||
# No closing `"` here — it's appended when the value closes.
|
||||
snapshot_parts.append(f'{k_json}: "{escaped}')
|
||||
|
||||
snapshot = "{" + ", ".join(snapshot_parts) + "}"
|
||||
|
||||
argument_diff: Optional[str] = None
|
||||
|
||||
if is_complete:
|
||||
final_json = json.dumps(self._completed_args, ensure_ascii=False)
|
||||
if self._streamed_json_len < len(final_json):
|
||||
argument_diff = final_json[self._streamed_json_len :]
|
||||
self._streamed_json_len = len(final_json)
|
||||
|
||||
while len(self.prev_tool_call_arr) <= self.current_tool_id:
|
||||
self.prev_tool_call_arr.append({})
|
||||
self.prev_tool_call_arr[self.current_tool_id] = {
|
||||
"name": self._streaming_tool_name,
|
||||
"arguments": dict(self._completed_args),
|
||||
}
|
||||
|
||||
end_idx = self._buffer.find(self.tool_call_end_token)
|
||||
self._buffer = self._buffer[end_idx + len(self.tool_call_end_token) :]
|
||||
self._reset_streaming_tool_state()
|
||||
else:
|
||||
# Withhold the trailing "}" while the tool call is still open.
|
||||
end = len(snapshot) - 1
|
||||
if end > self._streamed_json_len:
|
||||
argument_diff = snapshot[self._streamed_json_len : end]
|
||||
self._streamed_json_len = end
|
||||
|
||||
if argument_diff:
|
||||
self.streamed_args_for_tool[self.current_tool_id] += argument_diff
|
||||
return [
|
||||
ToolCallItem(
|
||||
tool_index=self.current_tool_id,
|
||||
parameters=argument_diff,
|
||||
)
|
||||
]
|
||||
return []
|
||||
|
||||
def structure_info(self) -> _GetInfoFunc:
|
||||
return lambda name: StructureInfo(
|
||||
begin=f"<tool_calls>\n<tool_call>{name}<tool_sep>",
|
||||
end="</tool_call>\n</tool_calls>",
|
||||
trigger="<tool_calls>",
|
||||
)
|
||||
|
||||
def supports_structural_tag(self) -> bool:
|
||||
return False
|
||||
+146
@@ -0,0 +1,146 @@
|
||||
{
|
||||
"1": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 32,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 64,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"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": 64,
|
||||
"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": 64,
|
||||
"GROUP_SIZE_M": 64,
|
||||
"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": 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": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"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": 128,
|
||||
"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": 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": 32,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"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": 4
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
}
|
||||
}
|
||||
+146
@@ -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": 5
|
||||
},
|
||||
"2": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"4": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"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": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"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": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 8,
|
||||
"num_stages": 2
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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": 2
|
||||
},
|
||||
"96": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"128": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"256": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"512": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"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": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 4
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"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": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
}
|
||||
}
|
||||
+146
@@ -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": 5
|
||||
},
|
||||
"2": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"4": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"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": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"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": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 8,
|
||||
"num_stages": 2
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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": 2
|
||||
},
|
||||
"96": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"128": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"256": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"512": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"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": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 4
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"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": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
}
|
||||
}
|
||||
+146
@@ -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": 128,
|
||||
"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": 5
|
||||
},
|
||||
"8": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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
|
||||
},
|
||||
"24": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"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": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"128": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"256": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"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": 8,
|
||||
"num_stages": 2
|
||||
},
|
||||
"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": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 4
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 64,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
}
|
||||
}
|
||||
+146
@@ -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": 128,
|
||||
"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": 5
|
||||
},
|
||||
"8": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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
|
||||
},
|
||||
"24": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"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": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"128": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"256": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 128,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"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": 8,
|
||||
"num_stages": 2
|
||||
},
|
||||
"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": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 4
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 64,
|
||||
"num_warps": 4,
|
||||
"num_stages": 4
|
||||
}
|
||||
}
|
||||
+146
@@ -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": 5
|
||||
},
|
||||
"2": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 32,
|
||||
"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": 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": 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": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 3
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"64": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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": 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": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 3
|
||||
},
|
||||
"512": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"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": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"1536": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
}
|
||||
}
|
||||
+146
@@ -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": 5
|
||||
},
|
||||
"2": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 32,
|
||||
"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": 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": 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": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 3
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"64": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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": 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": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 3
|
||||
},
|
||||
"512": {
|
||||
"BLOCK_SIZE_M": 32,
|
||||
"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": 1,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"1536": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"2048": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 8,
|
||||
"num_stages": 3
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"4096": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
}
|
||||
}
|
||||
+146
@@ -0,0 +1,146 @@
|
||||
{
|
||||
"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": 32,
|
||||
"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": 16,
|
||||
"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": 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": 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": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 3
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"64": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"96": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"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": 8,
|
||||
"num_stages": 4
|
||||
},
|
||||
"256": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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
|
||||
},
|
||||
"1024": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"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": 8,
|
||||
"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": 2
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"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": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
}
|
||||
}
|
||||
+146
@@ -0,0 +1,146 @@
|
||||
{
|
||||
"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": 32,
|
||||
"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": 16,
|
||||
"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": 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": 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": 1,
|
||||
"num_warps": 4,
|
||||
"num_stages": 3
|
||||
},
|
||||
"48": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"64": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
},
|
||||
"96": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 32,
|
||||
"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": 8,
|
||||
"num_stages": 4
|
||||
},
|
||||
"256": {
|
||||
"BLOCK_SIZE_M": 16,
|
||||
"BLOCK_SIZE_N": 64,
|
||||
"BLOCK_SIZE_K": 64,
|
||||
"GROUP_SIZE_M": 16,
|
||||
"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
|
||||
},
|
||||
"1024": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"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": 8,
|
||||
"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": 2
|
||||
},
|
||||
"3072": {
|
||||
"BLOCK_SIZE_M": 64,
|
||||
"BLOCK_SIZE_N": 128,
|
||||
"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": 16,
|
||||
"num_warps": 4,
|
||||
"num_stages": 2
|
||||
}
|
||||
}
|
||||
@@ -903,6 +903,30 @@ def biased_grouped_topk_gpu(
|
||||
routed_scaling_factor=routed_scaling_factor,
|
||||
apply_routed_scaling_factor_on_output=apply_routed_scaling_factor_on_output,
|
||||
)
|
||||
elif (
|
||||
_is_cuda
|
||||
and num_expert_group == 1
|
||||
and topk_group == 1
|
||||
and num_fused_shared_experts == 0
|
||||
and num_experts <= 512
|
||||
and topk <= 8
|
||||
):
|
||||
from sglang.jit_kernel.grouped_topk import grouped_topk as jit_grouped_topk
|
||||
|
||||
scaling = (
|
||||
routed_scaling_factor if routed_scaling_factor is not None else 1.0
|
||||
)
|
||||
if not apply_routed_scaling_factor_on_output:
|
||||
scaling = 1.0
|
||||
return jit_grouped_topk(
|
||||
gating_output.to(dtype=torch.float32),
|
||||
correction_bias.to(dtype=torch.float32),
|
||||
num_expert_group,
|
||||
topk_group,
|
||||
topk,
|
||||
renormalize,
|
||||
scaling,
|
||||
)
|
||||
else:
|
||||
return biased_grouped_topk_impl(
|
||||
hidden_states,
|
||||
|
||||
@@ -0,0 +1,587 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The HunYuan team.
|
||||
# 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.
|
||||
|
||||
from typing import Iterable, Optional, Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import PretrainedConfig
|
||||
|
||||
from sglang.srt.distributed import (
|
||||
get_moe_expert_parallel_world_size,
|
||||
get_moe_tensor_parallel_world_size,
|
||||
get_tensor_model_parallel_world_size,
|
||||
moe_expert_parallel_all_reduce,
|
||||
moe_tensor_model_parallel_all_reduce,
|
||||
)
|
||||
from sglang.srt.layers.activation import SiluAndMul
|
||||
from sglang.srt.layers.layernorm import RMSNorm
|
||||
from sglang.srt.layers.linear import (
|
||||
MergedColumnParallelLinear,
|
||||
QKVParallelLinear,
|
||||
ReplicatedLinear,
|
||||
RowParallelLinear,
|
||||
)
|
||||
from sglang.srt.layers.logits_processor import LogitsProcessor
|
||||
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
|
||||
from sglang.srt.layers.moe.topk import TopK
|
||||
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
||||
from sglang.srt.layers.radix_attention import RadixAttention
|
||||
from sglang.srt.layers.rotary_embedding import get_rope
|
||||
from sglang.srt.layers.vocab_parallel_embedding import (
|
||||
ParallelLMHead,
|
||||
VocabParallelEmbedding,
|
||||
)
|
||||
from sglang.srt.managers.schedule_batch import ForwardBatch
|
||||
from sglang.srt.model_executor.cuda_graph_runner import get_is_capture_mode
|
||||
from sglang.srt.model_loader.weight_utils import default_weight_loader
|
||||
from sglang.srt.utils import is_cuda
|
||||
from sglang.srt.utils.hf_transformers_utils import get_rope_config
|
||||
|
||||
|
||||
class HYV3FeedForward(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
hidden_size: int,
|
||||
intermediate_size: int,
|
||||
hidden_act: str,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
reduce_results: bool = True,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.gate_up_proj = MergedColumnParallelLinear(
|
||||
hidden_size,
|
||||
[intermediate_size] * 2,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.gate_up_proj",
|
||||
)
|
||||
self.down_proj = RowParallelLinear(
|
||||
intermediate_size,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
reduce_results=reduce_results,
|
||||
prefix=f"{prefix}.down_proj",
|
||||
)
|
||||
if hidden_act != "silu":
|
||||
raise ValueError(
|
||||
f"Unsupported activation: {hidden_act}. Only silu is supported for now."
|
||||
)
|
||||
self.act_fn = SiluAndMul()
|
||||
|
||||
def forward(self, x):
|
||||
gate_up, _ = self.gate_up_proj(x)
|
||||
out = self.act_fn(gate_up)
|
||||
out, _ = self.down_proj(out)
|
||||
return out
|
||||
|
||||
|
||||
class HYV3MoEFused(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
alt_stream: Optional[torch.cuda.Stream] = None,
|
||||
):
|
||||
super().__init__()
|
||||
self.tp_size = get_moe_tensor_parallel_world_size()
|
||||
self.ep_size = get_moe_expert_parallel_world_size()
|
||||
self.layer_id = layer_id
|
||||
self.alt_stream = alt_stream
|
||||
self.n_routed_experts = config.num_experts
|
||||
top_k = config.num_experts_per_tok
|
||||
intermediate_size = config.moe_intermediate_size
|
||||
|
||||
self.expert_bias = nn.Parameter(
|
||||
torch.empty(config.num_experts, dtype=torch.float32)
|
||||
)
|
||||
self.expert_bias.weight_loader = HYV3MoEFused.ebias_weight_loader
|
||||
scoring_func = "sigmoid"
|
||||
self.e_score_correction_bias = self.expert_bias
|
||||
self.router_scaling_factor = getattr(config, "router_scaling_factor", 1.0)
|
||||
self.gate = ReplicatedLinear(
|
||||
config.hidden_size,
|
||||
config.num_experts,
|
||||
bias=False,
|
||||
quant_config=None,
|
||||
params_dtype=torch.float32,
|
||||
prefix=f"{prefix}.gate",
|
||||
)
|
||||
self.topk = TopK(
|
||||
top_k=config.num_experts_per_tok,
|
||||
use_grouped_topk=True,
|
||||
num_expert_group=1,
|
||||
topk_group=1,
|
||||
renormalize=config.route_norm,
|
||||
scoring_func=scoring_func,
|
||||
correction_bias=self.e_score_correction_bias,
|
||||
routed_scaling_factor=self.router_scaling_factor,
|
||||
apply_routed_scaling_factor_on_output=True,
|
||||
)
|
||||
|
||||
if getattr(config, "num_shared_experts", 0) > 0:
|
||||
self.shared_mlp = HYV3FeedForward(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.moe_intermediate_size
|
||||
* config.num_shared_experts,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.shared_mlp",
|
||||
reduce_results=False,
|
||||
)
|
||||
else:
|
||||
self.shared_mlp = None
|
||||
|
||||
self.experts = FusedMoE(
|
||||
num_experts=self.n_routed_experts,
|
||||
top_k=top_k,
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=intermediate_size,
|
||||
reduce_results=False,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.experts",
|
||||
)
|
||||
|
||||
@staticmethod
|
||||
def ebias_weight_loader(param: nn.Parameter, loaded_weight: torch.Tensor) -> None:
|
||||
assert param.size() == loaded_weight.size()
|
||||
param.data.copy_(loaded_weight.to(torch.float32))
|
||||
|
||||
def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
if (
|
||||
self.alt_stream is not None
|
||||
and self.shared_mlp is not None
|
||||
and hidden_states.shape[0] > 0
|
||||
and get_is_capture_mode()
|
||||
):
|
||||
return self._forward_dual_stream(hidden_states)
|
||||
return self._forward_single_stream(hidden_states)
|
||||
|
||||
def _forward_single_stream(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
orig_shape = hidden_states.shape
|
||||
hidden_dim = hidden_states.shape[-1]
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
|
||||
router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
|
||||
topk_output = self.topk(hidden_states, router_logits)
|
||||
if self.shared_mlp is not None:
|
||||
shared_output = self.shared_mlp(hidden_states)
|
||||
final_hidden_states = self.experts(
|
||||
hidden_states=hidden_states, topk_output=topk_output
|
||||
)
|
||||
final_hidden_states = final_hidden_states + shared_output
|
||||
else:
|
||||
final_hidden_states = self.experts(
|
||||
hidden_states=hidden_states, topk_output=topk_output
|
||||
)
|
||||
|
||||
if self.ep_size > 1:
|
||||
final_hidden_states = moe_expert_parallel_all_reduce(final_hidden_states)
|
||||
|
||||
if self.tp_size > 1:
|
||||
final_hidden_states = moe_tensor_model_parallel_all_reduce(
|
||||
final_hidden_states
|
||||
)
|
||||
|
||||
return final_hidden_states.view(orig_shape)
|
||||
|
||||
def _forward_dual_stream(self, hidden_states: torch.Tensor) -> torch.Tensor:
|
||||
"""Shared experts on main stream, routed experts on alt stream."""
|
||||
orig_shape = hidden_states.shape
|
||||
hidden_dim = hidden_states.shape[-1]
|
||||
hidden_states = hidden_states.view(-1, hidden_dim)
|
||||
|
||||
current_stream = torch.cuda.current_stream()
|
||||
self.alt_stream.wait_stream(current_stream)
|
||||
|
||||
shared_output = self.shared_mlp(hidden_states)
|
||||
|
||||
with torch.cuda.stream(self.alt_stream):
|
||||
router_logits, _ = self.gate(hidden_states.to(dtype=torch.float32))
|
||||
topk_output = self.topk(hidden_states, router_logits)
|
||||
final_hidden_states = self.experts(
|
||||
hidden_states=hidden_states, topk_output=topk_output
|
||||
)
|
||||
|
||||
current_stream.wait_stream(self.alt_stream)
|
||||
final_hidden_states = final_hidden_states + shared_output
|
||||
|
||||
if self.ep_size > 1:
|
||||
final_hidden_states = moe_expert_parallel_all_reduce(final_hidden_states)
|
||||
|
||||
if self.tp_size > 1:
|
||||
final_hidden_states = moe_tensor_model_parallel_all_reduce(
|
||||
final_hidden_states
|
||||
)
|
||||
|
||||
return final_hidden_states.view(orig_shape)
|
||||
|
||||
|
||||
class HYV3Attention(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
hidden_size: int,
|
||||
num_heads: int,
|
||||
num_kv_heads: int,
|
||||
layer_id: int = 0,
|
||||
rope_theta: float = 10000,
|
||||
rope_scaling: Optional[dict] = None,
|
||||
max_position_embeddings: int = 8192,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.hidden_size = hidden_size
|
||||
tp_size = get_tensor_model_parallel_world_size()
|
||||
self.total_num_heads = num_heads
|
||||
assert self.total_num_heads % tp_size == 0
|
||||
self.num_heads = self.total_num_heads // tp_size
|
||||
self.total_num_kv_heads = num_kv_heads
|
||||
if self.total_num_kv_heads >= tp_size:
|
||||
assert self.total_num_kv_heads % tp_size == 0
|
||||
else:
|
||||
assert tp_size % self.total_num_kv_heads == 0
|
||||
self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size)
|
||||
|
||||
self.head_dim = getattr(config, "head_dim", hidden_size // self.total_num_heads)
|
||||
self.q_size = self.num_heads * self.head_dim
|
||||
self.kv_size = self.num_kv_heads * self.head_dim
|
||||
self.scaling = self.head_dim**-0.5
|
||||
self.use_qk_norm = getattr(
|
||||
config, "use_qk_norm", getattr(config, "qk_norm", False)
|
||||
)
|
||||
|
||||
self.qkv_proj = QKVParallelLinear(
|
||||
hidden_size,
|
||||
self.head_dim,
|
||||
self.total_num_heads,
|
||||
self.total_num_kv_heads,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.qkv_proj",
|
||||
)
|
||||
self.o_proj = RowParallelLinear(
|
||||
self.total_num_heads * self.head_dim,
|
||||
hidden_size,
|
||||
bias=False,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.o_proj",
|
||||
)
|
||||
|
||||
self.rotary_emb = get_rope(
|
||||
self.head_dim,
|
||||
rotary_dim=self.head_dim,
|
||||
max_position=max_position_embeddings,
|
||||
base=rope_theta,
|
||||
rope_scaling=rope_scaling,
|
||||
is_neox_style=True,
|
||||
)
|
||||
self.attn = RadixAttention(
|
||||
self.num_heads,
|
||||
self.head_dim,
|
||||
self.scaling,
|
||||
num_kv_heads=self.num_kv_heads,
|
||||
layer_id=layer_id,
|
||||
prefix=f"{prefix}.attn",
|
||||
)
|
||||
if self.use_qk_norm:
|
||||
rms_norm_eps = getattr(config, "rms_norm_eps", 1e-5)
|
||||
self.q_norm = RMSNorm(self.head_dim, rms_norm_eps)
|
||||
self.k_norm = RMSNorm(self.head_dim, rms_norm_eps)
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
) -> torch.Tensor:
|
||||
qkv, _ = self.qkv_proj(hidden_states)
|
||||
q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1)
|
||||
|
||||
if self.use_qk_norm:
|
||||
q = self.q_norm(q.reshape(-1, self.head_dim))
|
||||
q = q.view(-1, self.q_size)
|
||||
k = self.k_norm(k.reshape(-1, self.head_dim))
|
||||
k = k.view(-1, self.kv_size)
|
||||
|
||||
q, k = self.rotary_emb(positions, q, k)
|
||||
attn_output = self.attn(q, k, v, forward_batch)
|
||||
output, _ = self.o_proj(attn_output)
|
||||
return output
|
||||
|
||||
|
||||
class HYV3DecoderLayer(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
layer_id: int,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
alt_stream: Optional[torch.cuda.Stream] = None,
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.layer_id = layer_id
|
||||
self.hidden_size = config.hidden_size
|
||||
max_position_embeddings = getattr(config, "max_position_embeddings", 8192)
|
||||
rope_theta, _ = get_rope_config(config)
|
||||
self.self_attn = HYV3Attention(
|
||||
config=config,
|
||||
hidden_size=self.hidden_size,
|
||||
num_heads=config.num_attention_heads,
|
||||
num_kv_heads=config.num_key_value_heads,
|
||||
layer_id=layer_id,
|
||||
rope_theta=rope_theta,
|
||||
max_position_embeddings=max_position_embeddings,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.self_attn",
|
||||
)
|
||||
self.input_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
|
||||
self.post_attention_layernorm = RMSNorm(config.hidden_size, config.rms_norm_eps)
|
||||
|
||||
first_k_dense_replace = getattr(config, "first_k_dense_replace", 0)
|
||||
if layer_id < first_k_dense_replace:
|
||||
self.mlp = HYV3FeedForward(
|
||||
hidden_size=config.hidden_size,
|
||||
intermediate_size=config.intermediate_size,
|
||||
hidden_act=config.hidden_act,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
)
|
||||
self.block_type = "feedforward"
|
||||
else:
|
||||
self.mlp = HYV3MoEFused(
|
||||
config=config,
|
||||
layer_id=layer_id,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.mlp",
|
||||
alt_stream=alt_stream,
|
||||
)
|
||||
self.block_type = "moe"
|
||||
|
||||
def forward(
|
||||
self,
|
||||
positions: torch.Tensor,
|
||||
hidden_states: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
residual: Optional[torch.Tensor],
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
if residual is None:
|
||||
residual = hidden_states
|
||||
hidden_states = self.input_layernorm(hidden_states)
|
||||
else:
|
||||
hidden_states, residual = self.input_layernorm(hidden_states, residual)
|
||||
hidden_states = self.self_attn(
|
||||
positions=positions,
|
||||
hidden_states=hidden_states,
|
||||
forward_batch=forward_batch,
|
||||
)
|
||||
|
||||
hidden_states, residual = self.post_attention_layernorm(hidden_states, residual)
|
||||
hidden_states = self.mlp(hidden_states)
|
||||
|
||||
return hidden_states, residual
|
||||
|
||||
|
||||
class HYV3Model(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
prefix=f"{prefix}.embed_tokens",
|
||||
)
|
||||
|
||||
self.alt_stream = torch.cuda.Stream() if is_cuda() else None
|
||||
|
||||
self.layers = nn.ModuleList(
|
||||
[
|
||||
HYV3DecoderLayer(
|
||||
config=config,
|
||||
layer_id=i,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.layers.{i}",
|
||||
alt_stream=self.alt_stream,
|
||||
)
|
||||
for i in range(config.num_hidden_layers)
|
||||
]
|
||||
)
|
||||
self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
if input_embeds is None:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
else:
|
||||
hidden_states = input_embeds
|
||||
residual = None
|
||||
for layer in self.layers:
|
||||
hidden_states, residual = layer(
|
||||
positions, hidden_states, forward_batch, residual
|
||||
)
|
||||
|
||||
hidden_states, _ = self.norm(hidden_states, residual)
|
||||
return hidden_states
|
||||
|
||||
|
||||
class HYV3ForCausalLM(nn.Module):
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
):
|
||||
super().__init__()
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = HYV3Model(config, quant_config, prefix=f"{prefix}.model")
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.lm_head",
|
||||
)
|
||||
if getattr(self.config, "tie_word_embeddings", False):
|
||||
self.lm_head.weight = self.model.embed_tokens.weight
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, forward_batch, input_embeds)
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
|
||||
def get_embed_and_head(self):
|
||||
return self.model.embed_tokens.weight, self.lm_head.weight
|
||||
|
||||
def set_embed_and_head(self, embed, head):
|
||||
del self.model.embed_tokens.weight
|
||||
del self.lm_head.weight
|
||||
self.model.embed_tokens.weight = embed
|
||||
self.lm_head.weight = head
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
stacked_params_mapping = [
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
# Params for weights, fp8 weight scales, fp8 activation scales
|
||||
# (param_name, weight_name, expert_id, shard_id)
|
||||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="gate_proj",
|
||||
ckpt_down_proj_name="down_proj",
|
||||
ckpt_up_proj_name="up_proj",
|
||||
num_experts=self.config.num_experts,
|
||||
)
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
num_nextn_layers = getattr(self.config, "num_nextn_predict_layers", 0)
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
if "lm_head.weight" in name and getattr(
|
||||
self.config, "tie_word_embeddings", False
|
||||
):
|
||||
continue
|
||||
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
if num_nextn_layers > 0 and name.startswith("model.layers."):
|
||||
parts = name.split(".")
|
||||
if len(parts) >= 3 and int(parts[2]) >= self.config.num_hidden_layers:
|
||||
continue
|
||||
|
||||
is_found = False
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "mlp.experts" in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
is_found = True
|
||||
break
|
||||
if is_found:
|
||||
continue
|
||||
|
||||
# Handle expert weights (including fp8 weight_scale, input_scale)
|
||||
is_expert_weight = False
|
||||
for mapping in expert_params_mapping:
|
||||
param_name, weight_name, expert_id, shard_id = mapping
|
||||
if weight_name not in name:
|
||||
continue
|
||||
is_expert_weight = True
|
||||
name_mapped = name.replace(weight_name, param_name)
|
||||
if name_mapped not in params_dict:
|
||||
continue
|
||||
param = params_dict[name_mapped]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name_mapped,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
break
|
||||
if is_expert_weight:
|
||||
continue
|
||||
|
||||
if "router.gate." in name:
|
||||
name = name.replace("router.", "")
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = [HYV3ForCausalLM]
|
||||
@@ -0,0 +1,253 @@
|
||||
# coding=utf-8
|
||||
# Copyright 2026 The HunYuan team.
|
||||
# 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.
|
||||
|
||||
"""Inference-only HunyuanV3 NextN (MTP) Speculative Decoding."""
|
||||
|
||||
import logging
|
||||
from typing import Iterable, Optional, Tuple
|
||||
|
||||
import torch
|
||||
from torch import nn
|
||||
from transformers import PretrainedConfig
|
||||
|
||||
from sglang.srt.layers.layernorm import RMSNorm
|
||||
from sglang.srt.layers.logits_processor import LogitsProcessor
|
||||
from sglang.srt.layers.moe.fused_moe_triton.layer import FusedMoE
|
||||
from sglang.srt.layers.quantization.base_config import QuantizationConfig
|
||||
from sglang.srt.layers.vocab_parallel_embedding import (
|
||||
ParallelLMHead,
|
||||
VocabParallelEmbedding,
|
||||
)
|
||||
from sglang.srt.managers.schedule_batch import ForwardBatch
|
||||
from sglang.srt.model_loader.weight_utils import default_weight_loader
|
||||
from sglang.srt.models.hunyuan_v3 import HYV3DecoderLayer
|
||||
from sglang.srt.utils import is_cuda
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class HYV3ModelNextN(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
super().__init__()
|
||||
self.config = config
|
||||
|
||||
self.embed_tokens = VocabParallelEmbedding(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
prefix=f"{prefix}.embed_tokens",
|
||||
)
|
||||
|
||||
self.enorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.hnorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.eh_proj = nn.Linear(2 * config.hidden_size, config.hidden_size, bias=False)
|
||||
|
||||
self.alt_stream = torch.cuda.Stream() if is_cuda() else None
|
||||
|
||||
# Force MoE for the MTP layer: first_k_dense_replace=1 would make
|
||||
# layer_id=0 pick a dense MLP instead of MoE, so override it.
|
||||
orig_first_k = getattr(config, "first_k_dense_replace", 0)
|
||||
config.first_k_dense_replace = 0
|
||||
self.decoder = HYV3DecoderLayer(
|
||||
config=config,
|
||||
layer_id=0,
|
||||
quant_config=quant_config,
|
||||
prefix=f"{prefix}.decoder",
|
||||
alt_stream=self.alt_stream,
|
||||
)
|
||||
config.first_k_dense_replace = orig_first_k
|
||||
|
||||
self.shared_head = nn.Module()
|
||||
self.shared_head.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
input_embeds: torch.Tensor = None,
|
||||
) -> torch.Tensor:
|
||||
if input_embeds is None:
|
||||
hidden_states = self.embed_tokens(input_ids)
|
||||
else:
|
||||
hidden_states = input_embeds
|
||||
|
||||
if hidden_states.shape[0] > 0:
|
||||
hidden_states = self.eh_proj(
|
||||
torch.cat(
|
||||
(
|
||||
self.enorm(hidden_states),
|
||||
self.hnorm(forward_batch.spec_info.hidden_states),
|
||||
),
|
||||
dim=-1,
|
||||
)
|
||||
)
|
||||
|
||||
residual = None
|
||||
hidden_states, residual = self.decoder(
|
||||
positions, hidden_states, forward_batch, residual
|
||||
)
|
||||
|
||||
if not forward_batch.forward_mode.is_idle():
|
||||
if residual is not None:
|
||||
hidden_states, _ = self.shared_head.norm(hidden_states, residual)
|
||||
else:
|
||||
hidden_states = self.shared_head.norm(hidden_states)
|
||||
|
||||
return hidden_states
|
||||
|
||||
|
||||
class HYV3ForCausalLMNextN(nn.Module):
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
config: PretrainedConfig,
|
||||
quant_config: Optional[QuantizationConfig] = None,
|
||||
prefix: str = "",
|
||||
) -> None:
|
||||
nn.Module.__init__(self)
|
||||
self.config = config
|
||||
self.quant_config = quant_config
|
||||
|
||||
self.model = HYV3ModelNextN(config, quant_config, prefix="model")
|
||||
self.lm_head = ParallelLMHead(
|
||||
config.vocab_size,
|
||||
config.hidden_size,
|
||||
quant_config=quant_config,
|
||||
prefix="lm_head",
|
||||
)
|
||||
self.logits_processor = LogitsProcessor(config)
|
||||
|
||||
@torch.no_grad()
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.Tensor,
|
||||
positions: torch.Tensor,
|
||||
forward_batch: ForwardBatch,
|
||||
) -> torch.Tensor:
|
||||
hidden_states = self.model(input_ids, positions, forward_batch)
|
||||
return self.logits_processor(
|
||||
input_ids, hidden_states, self.lm_head, forward_batch
|
||||
)
|
||||
|
||||
def get_embed_and_head(self):
|
||||
return self.model.embed_tokens.weight, self.lm_head.weight
|
||||
|
||||
def set_embed_and_head(self, embed, head):
|
||||
del self.model.embed_tokens.weight
|
||||
del self.lm_head.weight
|
||||
self.model.embed_tokens.weight = embed
|
||||
self.lm_head.weight = head
|
||||
torch.cuda.empty_cache()
|
||||
torch.cuda.synchronize()
|
||||
|
||||
def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]):
|
||||
nextn_layer_id = self.config.num_hidden_layers
|
||||
nextn_prefix = f"model.layers.{nextn_layer_id}."
|
||||
spec_weight_names = ("enorm", "hnorm", "eh_proj")
|
||||
|
||||
stacked_params_mapping = [
|
||||
("qkv_proj", "q_proj", "q"),
|
||||
("qkv_proj", "k_proj", "k"),
|
||||
("qkv_proj", "v_proj", "v"),
|
||||
("gate_up_proj", "gate_proj", 0),
|
||||
("gate_up_proj", "up_proj", 1),
|
||||
]
|
||||
|
||||
expert_params_mapping = FusedMoE.make_expert_params_mapping(
|
||||
ckpt_gate_proj_name="gate_proj",
|
||||
ckpt_down_proj_name="down_proj",
|
||||
ckpt_up_proj_name="up_proj",
|
||||
num_experts=self.config.num_experts,
|
||||
)
|
||||
|
||||
params_dict = dict(self.named_parameters())
|
||||
|
||||
for name, loaded_weight in weights:
|
||||
if name.startswith(nextn_prefix):
|
||||
subname = name[len(nextn_prefix) :]
|
||||
if any(subname.startswith(s) for s in spec_weight_names):
|
||||
name = f"model.{subname}"
|
||||
else:
|
||||
name = f"model.decoder.{subname}"
|
||||
elif name == "model.shared_head.norm.weight":
|
||||
pass
|
||||
elif (
|
||||
"embed_tokens" in name
|
||||
or "shared_head.head" in name
|
||||
or "lm_head" in name
|
||||
):
|
||||
continue
|
||||
else:
|
||||
continue
|
||||
|
||||
if "rotary_emb.inv_freq" in name:
|
||||
continue
|
||||
|
||||
if "router.gate." in name:
|
||||
name = name.replace("router.", "")
|
||||
|
||||
is_found = False
|
||||
for param_name, weight_name, shard_id in stacked_params_mapping:
|
||||
if weight_name not in name:
|
||||
continue
|
||||
if "mlp.experts" in name:
|
||||
continue
|
||||
name = name.replace(weight_name, param_name)
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(param, loaded_weight, shard_id)
|
||||
is_found = True
|
||||
break
|
||||
if is_found:
|
||||
continue
|
||||
|
||||
is_expert_weight = False
|
||||
for mapping in expert_params_mapping:
|
||||
param_name, weight_name, expert_id, shard_id = mapping
|
||||
if weight_name not in name:
|
||||
continue
|
||||
is_expert_weight = True
|
||||
name_mapped = name.replace(weight_name, param_name)
|
||||
if name_mapped not in params_dict:
|
||||
continue
|
||||
param = params_dict[name_mapped]
|
||||
weight_loader = param.weight_loader
|
||||
weight_loader(
|
||||
param,
|
||||
loaded_weight,
|
||||
name_mapped,
|
||||
shard_id=shard_id,
|
||||
expert_id=expert_id,
|
||||
)
|
||||
break
|
||||
if is_expert_weight:
|
||||
continue
|
||||
|
||||
if name not in params_dict:
|
||||
continue
|
||||
param = params_dict[name]
|
||||
weight_loader = getattr(param, "weight_loader", default_weight_loader)
|
||||
weight_loader(param, loaded_weight)
|
||||
|
||||
|
||||
EntryClass = [HYV3ForCausalLMNextN]
|
||||
@@ -482,6 +482,31 @@ class MistralDetector(BaseReasoningFormatDetector):
|
||||
)
|
||||
|
||||
|
||||
class HunyuanDetector(BaseReasoningFormatDetector):
|
||||
"""
|
||||
Detector for Hunyuan models (e.g., tencent/Hunyuan-A13B-Instruct).
|
||||
|
||||
Like Glm45Detector but uses ``<tool_calls>`` (plural) as the tool start token.
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
stream_reasoning: bool = True,
|
||||
force_reasoning: bool = False,
|
||||
continue_final_message: bool = False,
|
||||
previous_content: str = "",
|
||||
):
|
||||
super().__init__(
|
||||
"<think>",
|
||||
"</think>",
|
||||
force_reasoning=force_reasoning,
|
||||
stream_reasoning=stream_reasoning,
|
||||
tool_start_token="<tool_calls>",
|
||||
continue_final_message=continue_final_message,
|
||||
previous_content=previous_content,
|
||||
)
|
||||
|
||||
|
||||
class Gemma4Detector(BaseReasoningFormatDetector):
|
||||
"""Gemma4 reasoning detector."""
|
||||
|
||||
@@ -518,6 +543,7 @@ class ReasoningParser:
|
||||
"deepseek-r1": DeepSeekR1Detector,
|
||||
"deepseek-v3": Qwen3Detector,
|
||||
"glm45": Glm45Detector,
|
||||
"hunyuan": HunyuanDetector,
|
||||
"gpt-oss": GptOssDetector,
|
||||
"kimi": KimiDetector,
|
||||
"kimi_k2": KimiK2Detector,
|
||||
|
||||
@@ -3410,6 +3410,7 @@ class ServerArgs:
|
||||
"BailingMoeV2_5ForCausalLM",
|
||||
"MistralLarge3ForCausalLM",
|
||||
"PixtralForConditionalGeneration",
|
||||
"HYV3ForCausalLM",
|
||||
]:
|
||||
if self.speculative_draft_model_path is None:
|
||||
self.speculative_draft_model_path = self.model_path
|
||||
|
||||
Reference in New Issue
Block a user