add inference code

inference/README.md

@@ -0,0 +1,26 @@
+# Inference code for DeepSeek models
+
+First convert huggingface model weight files to the format of this project.
+```bash
+export EXPERTS=384
+export MP=8
+export CONFIG=config.json
+python convert.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP}
+```
+
+Then chat with DeepSeek model at will!
+```bash
+torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive
+```
+
+Or batch inference from file.
+```bash
+torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --input-file ${FILE}
+```
+
+Or multi nodes inference.
+```bash
+torchrun --nnodes ${NODES} --nproc-per-node $((MP / NODES)) --node-rank $RANK --master-addr $ADDR generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --input-file ${FILE}
+```
+
+If you want to use fp8, just remove `"expert_dtype": "fp4"` in `config.json` and specify `--expert-dtype fp8` in `convert.py`.

inference/config.json

@@ -0,0 +1,35 @@
+{
+    "vocab_size": 129280,
+    "dim": 7168,
+    "moe_inter_dim": 3072,
+    "n_layers": 61,
+    "n_hash_layers": 3,
+    "n_heads": 128,
+    "n_routed_experts": 384,
+    "n_shared_experts": 1,
+    "n_activated_experts": 6,
+    "score_func": "sqrtsoftplus",
+    "route_scale": 2.5,
+    "swiglu_limit": 10.0,
+    "q_lora_rank": 1536,
+    "head_dim": 512,
+    "rope_head_dim": 64,
+    "o_groups": 16,
+    "o_lora_rank": 1024,
+    "window_size": 128,
+    "original_seq_len": 65536,
+    "rope_theta": 10000,
+    "rope_factor": 16,
+    "beta_fast": 32,
+    "beta_slow": 1,
+    "index_n_heads": 64,
+    "index_head_dim": 128,
+    "index_topk": 512,
+    "hc_mult": 4,
+    "hc_sinkhorn_iters": 20,
+    "dtype": "fp8",
+    "scale_fmt": "ue8m0",
+    "expert_dtype": "fp4",
+    "compress_rope_theta": 160000,
+    "compress_ratios": [128, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 128, 4, 0]
+}

inference/convert.py

@@ -0,0 +1,168 @@
+import os
+import shutil
+from argparse import ArgumentParser
+from glob import glob
+from tqdm import tqdm, trange
+
+import torch
+from safetensors.torch import safe_open, save_file
+
+
+FP4_TABLE = torch.tensor([
+    0.0, 0.5, 1.0, 1.5, 2.0, 3.0, 4.0, 6.0,
+    0.0, -0.5, -1.0, -1.5, -2.0, -3.0, -4.0, -6.0
+], dtype=torch.float32)
+
+
+def cast_e2m1fn_to_e4m3fn(x: torch.Tensor, scale: torch.Tensor) -> tuple[torch.Tensor, torch.Tensor]:
+    """
+    Casts a tensor from e2m1fn to e4m3fn losslessly.
+    """
+    assert x.dtype == torch.int8
+    assert x.ndim == 2
+    out_dim, in_dim = x.size()
+    in_dim *= 2
+    fp8_block_size = 128
+    fp4_block_size = 32
+    assert in_dim % fp8_block_size == 0 and out_dim % fp8_block_size == 0
+    assert scale.size(0) == out_dim and scale.size(1) == in_dim // fp4_block_size
+
+    x = x.view(torch.uint8)
+    low  = x & 0x0F
+    high = (x >> 4) & 0x0F
+    x = torch.stack([FP4_TABLE[low.long()], FP4_TABLE[high.long()]], dim=-1).flatten(2)
+
+    # max_fp4 (6.0) * MAX_OFFSET must fit in e4m3fn (max 448)
+    # 6.0 * 2^6 = 384 < 448; 6.0 * 2^7 = 768 > 448; so MAX_OFFSET_BITS = 6
+    MAX_OFFSET_BITS = 6
+
+    bOut = out_dim // fp8_block_size
+    bIn = in_dim // fp8_block_size
+    # bOut, bIn, 128, 128
+    x = x.view(bOut, fp8_block_size, bIn, fp8_block_size).transpose(1, 2)
+    # bOut, bIn, 128*4
+    scale = scale.float().view(bOut, fp8_block_size, bIn, -1).transpose(1, 2).flatten(2)
+    ## bOut, bIn, 1
+    scale_max_offset_bits = scale.amax(dim=-1, keepdim=True) / (2**MAX_OFFSET_BITS)
+    # bOut, bIn, 128*4
+    offset = scale / scale_max_offset_bits
+    # bOut, bIn, 128, 128
+    offset = offset.unflatten(-1, (fp8_block_size, -1)).repeat_interleave(fp4_block_size, dim=-1)
+    x = (x * offset).transpose(1, 2).reshape(out_dim, in_dim)
+    return x.to(torch.float8_e4m3fn), scale_max_offset_bits.squeeze(-1).to(torch.float8_e8m0fnu)
+
+
+mapping = {
+    "embed_tokens": ("embed", 0),
+    "input_layernorm": ("attn_norm", None),
+    "post_attention_layernorm": ("ffn_norm", None),
+    "q_proj": ("wq", 0),
+    "q_a_proj": ("wq_a", None),
+    "q_a_layernorm": ("q_norm", None),
+    "q_b_proj": ("wq_b", 0),
+    "kv_a_proj_with_mqa": ("wkv_a", None),
+    "kv_a_layernorm": ("kv_norm", None),
+    "kv_b_proj": ("wkv_b", 0),
+    "o_proj": ("wo", 1),
+    "gate_proj": ("w1", 0),
+    "down_proj": ("w2", 1),
+    "up_proj": ("w3", 0),
+    "lm_head": ("head", 0),
+
+    "embed": ("embed", 0),
+    "wq_b": ("wq_b", 0),
+    "wo_a": ("wo_a", 0),
+    "wo_b": ("wo_b", 1),
+    "head": ("head", 0),
+    "attn_sink": ("attn_sink", 0),
+    "weights_proj": ("weights_proj", 0),
+}
+
+
+def main(hf_ckpt_path, save_path, n_experts, mp, expert_dtype):
+    """
+    Converts and saves model checkpoint files into a specified format.
+
+    Args:
+        hf_ckpt_path (str): Path to the directory containing the input checkpoint files.
+        save_path (str): Path to the directory where the converted checkpoint files will be saved.
+        n_experts (int): Total number of experts in the model.
+        mp (int): Model parallelism factor.
+        
+    Returns:
+        None
+    """
+    torch.set_num_threads(8)
+    n_local_experts = n_experts // mp
+    state_dicts = [{} for _ in range(mp)]
+
+    for file_path in tqdm(glob(os.path.join(hf_ckpt_path, "*.safetensors"))):
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            for name in f.keys():
+                param: torch.Tensor = f.get_tensor(name)
+                if name.startswith("model."):
+                    name = name[len("model."):]
+                if name.startswith("mtp.") and ("emb" in name or name.endswith("head.weight")):
+                    continue
+                name = name.replace("self_attn", "attn")
+                name = name.replace("mlp", "ffn")
+                name = name.replace("weight_scale_inv", "scale")
+                name = name.replace("e_score_correction_bias", "bias")
+                if any(x in name for x in ["hc", "attn_sink", "tie2eid", "ape"]):    # without .weight
+                    key = name.split(".")[-1]
+                else:
+                    key = name.split(".")[-2]
+                if key in mapping:
+                    new_key, dim = mapping[key]
+                else:
+                    new_key, dim = key, None
+                name = name.replace(key, new_key)
+                for i in range(mp):
+                    new_param = param
+                    if "experts" in name and "shared_experts" not in name:
+                        idx = int(name.split(".")[-3])
+                        if idx < i * n_local_experts or idx >= (i + 1) * n_local_experts:
+                            continue
+                    elif dim is not None:
+                        assert param.size(dim) % mp == 0, f"Dimension {dim} must be divisible by {mp}"
+                        shard_size = param.size(dim) // mp
+                        new_param = param.narrow(dim, i * shard_size, shard_size).contiguous()
+                    state_dicts[i][name] = new_param
+
+    os.makedirs(save_path, exist_ok=True)
+
+    for i in trange(mp):
+        names = list(state_dicts[i].keys())
+        for name in names:
+            if name.endswith("wo_a.weight"):
+                weight = state_dicts[i][name]
+                scale = state_dicts[i].pop(name.replace("weight", "scale"))
+                weight = weight.unflatten(0, (-1, 128)).unflatten(-1, (-1, 128)).float() * scale[:, None, :, None].float()
+                state_dicts[i][name] = weight.flatten(2, 3).flatten(0, 1).bfloat16()
+            elif "experts" in name and state_dicts[i][name].dtype == torch.int8:
+                if expert_dtype == "fp8":
+                    scale_name = name.replace("weight", "scale")
+                    weight = state_dicts[i].pop(name)
+                    scale = state_dicts[i].pop(scale_name)
+                    state_dicts[i][name], state_dicts[i][scale_name] = cast_e2m1fn_to_e4m3fn(weight, scale)
+                else:
+                    state_dicts[i][name] = state_dicts[i][name].view(torch.float4_e2m1fn_x2)
+        save_file(state_dicts[i], os.path.join(save_path, f"model{i}-mp{mp}.safetensors"))
+
+    for file in ["tokenizer.json", "tokenizer_config.json"]:
+        old_file_path = os.path.join(hf_ckpt_path, file)
+        new_file_path = os.path.join(save_path, file)
+        if os.path.exists(old_file_path):
+            shutil.copyfile(old_file_path, new_file_path)
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--hf-ckpt-path", type=str, required=True)
+    parser.add_argument("--save-path", type=str, required=True)
+    parser.add_argument("--n-experts", type=int, required=True)
+    parser.add_argument("--model-parallel", type=int, required=True)
+    parser.add_argument("--expert-dtype", type=str, choices=["fp8", "fp4"], required=False, default=None)
+    args = parser.parse_args()
+    assert args.n_experts % args.model_parallel == 0, "Number of experts must be divisible by model parallelism"
+    main(args.hf_ckpt_path, args.save_path, args.n_experts, args.model_parallel, args.expert_dtype)

inference/generate.py

@@ -0,0 +1,151 @@
+import os
+import json
+from argparse import ArgumentParser
+from typing import List
+
+import torch
+import torch.distributed as dist
+from transformers import AutoTokenizer
+from safetensors.torch import load_model
+
+from model import Transformer, ModelArgs
+from encoding_dsv4 import encode_messages, parse_message_from_completion_text
+
+
+def sample(logits, temperature: float = 1.0):
+    """Gumbel-max trick: equivalent to multinomial sampling but faster on GPU,
+    since it avoids the GPU-to-CPU sync in torch.multinomial."""
+    logits = logits / max(temperature, 1e-5)
+    probs = torch.softmax(logits, dim=-1, dtype=torch.float32)
+    return probs.div_(torch.empty_like(probs).exponential_(1)).argmax(dim=-1)
+
+
+@torch.inference_mode()
+def generate(
+    model: Transformer,
+    prompt_tokens: List[List[int]],
+    max_new_tokens: int,
+    eos_id: int,
+    temperature: float = 1.0
+) -> List[List[int]]:
+    """Batch generation with left-padded prompts.
+
+    The first forward pass processes [min_prompt_len:] tokens (prefill phase).
+    Subsequent passes generate one token at a time (decode phase). For positions
+    still within a prompt, the ground-truth token overrides the model's prediction.
+    """
+    prompt_lens = [len(t) for t in prompt_tokens]
+    assert max(prompt_lens) <= model.max_seq_len, f"Prompt length exceeds model maximum sequence length (max_seq_len={model.max_seq_len})"
+    total_len = min(model.max_seq_len, max_new_tokens + max(prompt_lens))
+    tokens = torch.full((len(prompt_tokens), total_len), -1, dtype=torch.long)
+    for i, t in enumerate(prompt_tokens):
+        tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long)
+    prev_pos = 0
+    finished = torch.tensor([False] * len(prompt_tokens))
+    prompt_mask = tokens != -1
+    for cur_pos in range(min(prompt_lens), total_len):
+        logits = model.forward(tokens[:, prev_pos:cur_pos], prev_pos)
+        if temperature > 0:
+            next_token = sample(logits, temperature)
+        else:
+            next_token = logits.argmax(dim=-1)
+        next_token = torch.where(prompt_mask[:, cur_pos], tokens[:, cur_pos], next_token)
+        tokens[:, cur_pos] = next_token
+        finished |= torch.logical_and(~prompt_mask[:, cur_pos], next_token == eos_id)
+        prev_pos = cur_pos
+        if finished.all():
+            break
+    completion_tokens = []
+    for i, toks in enumerate(tokens.tolist()):
+        toks = toks[prompt_lens[i]:prompt_lens[i]+max_new_tokens]
+        if eos_id in toks:
+            toks = toks[:toks.index(eos_id)]
+        toks.append(eos_id)
+        completion_tokens.append(toks)
+    return completion_tokens
+
+
+def main(
+    ckpt_path: str,
+    config: str,
+    input_file: str = "",
+    interactive: bool = True,
+    max_new_tokens: int = 100,
+    temperature: float = 1.0,
+) -> None:
+    world_size = int(os.getenv("WORLD_SIZE", "1"))
+    rank = int(os.getenv("RANK", "0"))
+    local_rank = int(os.getenv("LOCAL_RANK", "0"))
+    if world_size > 1:
+        dist.init_process_group("nccl")
+    global print
+    if rank != 0:
+        print = lambda *_, **__: None
+    torch.cuda.set_device(local_rank)
+    torch.cuda.memory._set_allocator_settings("expandable_segments:True")
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_num_threads(8)
+    torch.manual_seed(33377335)
+    with open(config) as f:
+        args = ModelArgs(**json.load(f))
+    if interactive:
+        args.max_batch_size = 1
+    print(args)
+    with torch.device("cuda"):
+        model = Transformer(args)
+    tokenizer = AutoTokenizer.from_pretrained(ckpt_path)
+    print("load model")
+    load_model(model, os.path.join(ckpt_path, f"model{rank}-mp{world_size}.safetensors"), strict=False)
+    torch.set_default_device("cuda")
+    print("I'm DeepSeek 👋")
+
+    if interactive:
+        messages = []
+        while True:
+            if world_size == 1:
+                prompt = input(">>> ")
+            elif rank == 0:
+                prompt = input(">>> ")
+                objects = [prompt]
+                dist.broadcast_object_list(objects, 0)
+            else:
+                objects = [None]
+                dist.broadcast_object_list(objects, 0)
+                prompt = objects[0]
+            if prompt == "/exit":
+                break
+            elif prompt == "/clear":
+                messages.clear()
+                continue
+            messages.append({"role": "user", "content": prompt})
+            prompt_tokens = tokenizer.encode(encode_messages(messages, thinking_mode="chat"))
+            completion_tokens = generate(model, [prompt_tokens], max_new_tokens, tokenizer.eos_token_id, temperature)
+            completion = tokenizer.decode(completion_tokens[0])
+            print(completion)
+            messages.append(parse_message_from_completion_text(completion, thinking_mode="chat"))
+    else:
+        with open(input_file) as f:
+            prompts = f.read().split("\n\n")
+        prompt_tokens = [tokenizer.encode(encode_messages([{"role": "user", "content": prompt}], thinking_mode="chat")) for prompt in prompts]
+        completion_tokens = generate(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id, temperature)
+        completions = tokenizer.batch_decode(completion_tokens)
+        for prompt, completion in zip(prompts, completions):
+            print("Prompt:", prompt)
+            print("Completion:", completion)
+            print()
+
+    if world_size > 1:
+        dist.destroy_process_group()
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--ckpt-path", type=str, required=True)
+    parser.add_argument("--config", type=str, required=True)
+    parser.add_argument("--input-file", type=str, default="")
+    parser.add_argument("--interactive", action="store_true")
+    parser.add_argument("--max-new-tokens", type=int, default=300)
+    parser.add_argument("--temperature", type=float, default=0.6)
+    args = parser.parse_args()
+    assert args.input_file or args.interactive, "Either input-file or interactive mode must be specified"
+    main(args.ckpt_path, args.config, args.input_file, args.interactive, args.max_new_tokens, args.temperature)

inference/kernel.py

@@ -0,0 +1,536 @@
+import torch
+import tilelang
+import tilelang.language as T
+from typing import Tuple, Optional
+
+
+tilelang.set_log_level("WARNING")
+
+pass_configs = {
+    tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED: True,
+    tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER: True,
+}
+
+FP8 = "float8_e4m3"
+FP4 = "float4_e2m1fn"
+FE8M0 = "float8_e8m0fnu"
+BF16 = "bfloat16"
+FP32 = "float32"
+INT32 = "int32"
+
+
+def fast_log2_ceil(x):
+    """Compute ceil(log2(x)) via IEEE 754 bit manipulation. Avoids slow log/ceil intrinsics."""
+    bits_x = T.reinterpret("uint32", x)
+    exp_x = (bits_x >> 23) & 0xFF
+    man_bits = bits_x & ((1 << 23) - 1)
+    return T.Cast("int32", exp_x - 127 + T.if_then_else(man_bits != 0, 1, 0))
+
+
+def fast_pow2(x):
+    """Compute 2^x for integer x via IEEE 754 bit manipulation."""
+    bits_x = (x + 127) << 23
+    return T.reinterpret("float32", bits_x)
+
+
+def fast_round_scale(amax, fp8_max_inv):
+    return fast_pow2(fast_log2_ceil(amax * fp8_max_inv))
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def act_quant_kernel(
+    N, block_size=128, in_dtype=BF16, out_dtype=FP8, scale_dtype=FP32,
+    round_scale=False, inplace=False
+):
+    """Block-wise FP8 quantization. inplace=True does fused quant+dequant back to BF16."""
+    M = T.symbolic("M")
+    fp8_min = -448.0
+    fp8_max = 448.0
+    fp8_max_inv = 1 / fp8_max
+    num_stages = 0 if round_scale or inplace else 2
+    blk_m = 32
+    group_size = block_size
+    # Internal computation in FP32; scale_dtype controls output storage format.
+    compute_dtype = FP32
+    out_dtype = in_dtype if inplace else out_dtype
+
+    @T.prim_func
+    def act_quant_kernel_(
+        X: T.Tensor[(M, N), in_dtype],
+        Y: T.Tensor[(M, N), out_dtype],
+        S: T.Tensor[(M, T.ceildiv(N, group_size)), scale_dtype],
+    ):
+        with T.Kernel(T.ceildiv(M, blk_m), T.ceildiv(N, group_size), threads=128) as (
+            pid_m,
+            pid_n,
+        ):
+            x_shared = T.alloc_shared((blk_m, group_size), in_dtype)
+            x_local = T.alloc_fragment((blk_m, group_size), in_dtype)
+            amax_local = T.alloc_fragment((blk_m,), compute_dtype)
+            s_local = T.alloc_fragment((blk_m,), compute_dtype)
+            y_local = T.alloc_fragment((blk_m, group_size), out_dtype)
+            y_shared = T.alloc_shared((blk_m, group_size), out_dtype)
+
+            for _ in T.Pipelined(1, num_stages=num_stages):
+                T.copy(X[pid_m * blk_m, pid_n * group_size], x_shared)
+                T.copy(x_shared, x_local)
+                T.reduce_absmax(x_local, amax_local, dim=1)
+                for i in T.Parallel(blk_m):
+                    amax_local[i] = T.max(amax_local[i], 1e-4)
+                    if round_scale:
+                        s_local[i] = fast_round_scale(amax_local[i], fp8_max_inv)
+                    else:
+                        s_local[i] = amax_local[i] * fp8_max_inv
+                if inplace:
+                    for i, j in T.Parallel(blk_m, group_size):
+                        y_local[i, j] = T.Cast(
+                            out_dtype,
+                            T.Cast(compute_dtype, T.Cast(out_dtype, T.clamp(
+                                x_local[i, j] / s_local[i], fp8_min, fp8_max
+                            ))) * s_local[i],
+                        )
+                else:
+                    for i, j in T.Parallel(blk_m, group_size):
+                        y_local[i, j] = T.clamp(
+                            x_local[i, j] / s_local[i], fp8_min, fp8_max
+                        )
+                for i in T.Parallel(blk_m):
+                    S[pid_m * blk_m + i, pid_n] = T.Cast(scale_dtype, s_local[i])
+                T.copy(y_local, y_shared)
+                T.copy(y_shared, Y[pid_m * blk_m, pid_n * group_size])
+
+    return act_quant_kernel_
+
+
+def act_quant(
+    x: torch.Tensor, block_size: int = 128, scale_fmt: Optional[str] = None,
+    scale_dtype: torch.dtype = torch.float32, inplace: bool = False,
+) -> torch.Tensor:
+    """Block-wise FP8 quantization. inplace=True does fused quant+dequant back to BF16.
+    When scale_fmt is set, scales are rounded to power-of-2 (MXFP)."""
+    N = x.size(-1)
+    assert N % block_size == 0
+    tl_dtype = FE8M0 if scale_dtype == torch.float8_e8m0fnu else FP32
+    z = x.contiguous()
+    y = torch.empty_like(z) if inplace else torch.empty_like(z, dtype=torch.float8_e4m3fn)
+    s = z.new_empty(*z.size()[:-1], N // block_size, dtype=scale_dtype)
+    kernel = act_quant_kernel(
+        N, block_size, scale_dtype=tl_dtype,
+        round_scale=scale_fmt is not None, inplace=inplace,
+    )
+    kernel(z.view(-1, N), y.view(-1, N), s.view(-1, N // block_size))
+    if inplace:
+        x.copy_(y)
+        return x
+    return y, s
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def fp4_quant_kernel(
+    N, block_size=32, in_dtype=BF16, scale_dtype=FE8M0, inplace=False
+):
+    """Block-wise FP4 quantization. Power-of-2 scale via bit ops. inplace=True does fused quant+dequant."""
+    M = T.symbolic("M")
+    fp4_max = 6.0
+    fp4_max_inv = 1.0 / fp4_max
+    blk_m = 32
+    group_size = block_size
+    compute_dtype = FP32
+    out_dtype = in_dtype if inplace else FP4
+
+    @T.prim_func
+    def fp4_quant_kernel_(
+        X: T.Tensor[(M, N), in_dtype],
+        Y: T.Tensor[(M, N), out_dtype],
+        S: T.Tensor[(M, T.ceildiv(N, group_size)), scale_dtype],
+    ):
+        with T.Kernel(T.ceildiv(M, blk_m), T.ceildiv(N, group_size), threads=128) as (
+            pid_m,
+            pid_n,
+        ):
+            x_shared = T.alloc_shared((blk_m, group_size), in_dtype)
+            x_local = T.alloc_fragment((blk_m, group_size), in_dtype)
+            amax_local = T.alloc_fragment((blk_m,), compute_dtype)
+            s_local = T.alloc_fragment((blk_m,), compute_dtype)
+            y_local = T.alloc_fragment((blk_m, group_size), out_dtype)
+            y_shared = T.alloc_shared((blk_m, group_size), out_dtype)
+
+            for _ in T.Pipelined(1, num_stages=2):
+                T.copy(X[pid_m * blk_m, pid_n * group_size], x_shared)
+                T.copy(x_shared, x_local)
+                T.reduce_absmax(x_local, amax_local, dim=1)
+                for i in T.Parallel(blk_m):
+                    amax_local[i] = T.max(amax_local[i], 6 * (2**-126))
+                    s_local[i] = fast_round_scale(amax_local[i], fp4_max_inv)
+                if inplace:
+                    for i, j in T.Parallel(blk_m, group_size):
+                        y_local[i, j] = T.Cast(
+                            out_dtype,
+                            T.Cast(compute_dtype, T.Cast(FP4, T.clamp(
+                                x_local[i, j] / s_local[i], -fp4_max, fp4_max
+                            ))) * s_local[i],
+                        )
+                else:
+                    for i, j in T.Parallel(blk_m, group_size):
+                        y_local[i, j] = T.clamp(
+                            x_local[i, j] / s_local[i], -fp4_max, fp4_max
+                        )
+                for i in T.Parallel(blk_m):
+                    S[pid_m * blk_m + i, pid_n] = T.Cast(scale_dtype, s_local[i])
+                T.copy(y_local, y_shared)
+                T.copy(y_shared, Y[pid_m * blk_m, pid_n * group_size])
+
+    return fp4_quant_kernel_
+
+
+def fp4_act_quant(
+    x: torch.Tensor, block_size: int = 32, inplace: bool = False,
+) -> torch.Tensor:
+    """Block-wise FP4 quantization. inplace=True does fused quant+dequant back to BF16."""
+    N = x.size(-1)
+    assert N % block_size == 0
+    z = x.contiguous()
+    y = torch.empty_like(z) if inplace else z.new_empty(*z.shape[:-1], N // 2, dtype=torch.float4_e2m1fn_x2)
+    s = z.new_empty(*z.size()[:-1], N // block_size, dtype=torch.float8_e8m0fnu)
+    kernel = fp4_quant_kernel(N, block_size, inplace=inplace)
+    kernel(z.view(-1, N), y.view(-1, y.size(-1)), s.view(-1, N // block_size))
+    if inplace:
+        x.copy_(y)
+        return x
+    return y, s
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def fp8_gemm_kernel(N, K, out_dtype=BF16, accum_dtype=FP32, scale_dtype=FP32):
+    assert out_dtype in [BF16, FP32]
+
+    M = T.symbolic("M")
+    group_size = 128
+    block_M = 32
+    block_N = 128
+    block_K = 128
+
+    @T.prim_func
+    def fp8_gemm_kernel_(
+        A: T.Tensor[(M, K), FP8],
+        B: T.Tensor[(N, K), FP8],
+        C: T.Tensor[(M, N), out_dtype],
+        scales_a: T.Tensor[(M, T.ceildiv(K, group_size)), scale_dtype],
+        scales_b: T.Tensor[(T.ceildiv(N, group_size), T.ceildiv(K, group_size)), scale_dtype],
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (
+            bx,
+            by,
+        ):
+            A_shared = T.alloc_shared((block_M, block_K), FP8)
+            B_shared = T.alloc_shared((block_N, block_K), FP8)
+            C_shared = T.alloc_shared((block_M, block_N), out_dtype)
+            Scale_C_shared = T.alloc_shared((block_M), FP32)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+            C_local_accum = T.alloc_fragment((block_M, block_N), accum_dtype)
+
+            # Improve L2 Cache
+            T.use_swizzle(panel_size=10)
+            T.clear(C_local)
+            T.clear(C_local_accum)
+
+            K_iters = T.ceildiv(K, block_K)
+            for k in T.Pipelined(K_iters, num_stages=4):
+                T.copy(A[by * block_M, k * block_K], A_shared)
+                T.copy(B[bx * block_N, k * block_K], B_shared)
+                # Cast scales to FP32 for computation; scales_b has one value per block_N group
+                Scale_B = T.Cast(FP32, scales_b[bx * block_N // group_size, k])
+                for i in T.Parallel(block_M):
+                    Scale_C_shared[i] = T.Cast(FP32, scales_a[by * block_M + i, k]) * Scale_B
+
+                T.gemm(A_shared, B_shared, C_local, transpose_B=True)
+                # Separate accumulator for scale-corrected results (2x accumulation precision)
+                for i, j in T.Parallel(block_M, block_N):
+                    C_local_accum[i, j] += C_local[i, j] * Scale_C_shared[i]
+                T.clear(C_local)
+            T.copy(C_local_accum, C_shared)
+            T.copy(C_shared, C[by * block_M, bx * block_N])
+
+    return fp8_gemm_kernel_
+
+
+def fp8_gemm(
+    a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor,
+    scale_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """C[M,N] = A[M,K] @ B[N,K]^T with per-128 block FP8 scaling on both A and B."""
+    assert a.is_contiguous() and b.is_contiguous(), "Input tensors must be contiguous"
+    assert a_s.is_contiguous() and b_s.is_contiguous(), (
+        "Scaling factor tensors must be contiguous"
+    )
+    tl_dtype = FE8M0 if scale_dtype == torch.float8_e8m0fnu else FP32
+    K = a.size(-1)
+    M = a.numel() // K
+    N = b.size(0)
+    c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype())
+    kernel = fp8_gemm_kernel(N, K, scale_dtype=tl_dtype)
+    kernel(a.view(M, K), b, c.view(M, N), a_s.view(M, -1), b_s)
+    return c
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def sparse_attn_kernel(h: int, d: int, scale=None):
+    """Sparse multi-head attention via index gathering + online softmax (FlashAttention-style).
+    For each (batch, seq_pos), gathers top-k KV positions by index, computes attention
+    with numerically stable running max/sum, and includes a learnable attn_sink bias."""
+    b = T.symbolic("b")
+    m = T.symbolic("m")
+    n = T.symbolic("n")
+    topk = T.symbolic("topk")
+    if scale is None:
+        scale = (1.0 / d) ** 0.5
+
+    num_stages = 2
+    threads = 256
+    block = 64
+    num_blocks = tilelang.cdiv(topk, block)
+
+    @T.prim_func
+    def sparse_attn_kernel_(
+        q: T.Tensor[(b, m, h, d), BF16],
+        kv: T.Tensor[(b, n, d), BF16],
+        o: T.Tensor[(b, m, h, d), BF16],
+        attn_sink: T.Tensor[(h,), FP32],
+        topk_idxs: T.Tensor[(b, m, topk), INT32],
+    ):
+        with T.Kernel(m, b, threads=threads) as (bx, by):
+            q_shared = T.alloc_shared((h, d), BF16)
+            kv_shared = T.alloc_shared((block, d), BF16)
+            o_shared = T.alloc_shared((h, d), BF16)
+            acc_s_cast = T.alloc_shared((h, block), BF16)
+
+            idxs = T.alloc_fragment(block, INT32)
+            acc_s = T.alloc_fragment((h, block), FP32)
+            acc_o = T.alloc_fragment((h, d), FP32)
+            scores_max = T.alloc_fragment(h, FP32)
+            scores_max_prev = T.alloc_fragment(h, FP32)
+            scores_scale = T.alloc_fragment(h, FP32)
+            scores_sum = T.alloc_fragment(h, FP32)
+            sum_exp = T.alloc_fragment(h, FP32)
+
+            T.clear(acc_o)
+            T.clear(sum_exp)
+            T.fill(scores_max, -T.infinity(FP32))
+            T.copy(q[by, bx, :, :], q_shared)
+
+            for t in T.Pipelined(num_blocks, num_stages=num_stages):
+                for i in T.Parallel(block):
+                    idxs[i] = T.if_then_else(t * block + i < topk, topk_idxs[by, bx, t * block + i], -1)
+                for i, j in T.Parallel(block, d):
+                    kv_shared[i, j] = T.if_then_else(idxs[i] != -1, kv[by, idxs[i], j], 0)
+                for i, j in T.Parallel(h, block):
+                    acc_s[i, j] = T.if_then_else(idxs[j] != -1, 0, -T.infinity(FP32))
+                T.gemm(q_shared, kv_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+                for i, j in T.Parallel(h, block):
+                    acc_s[i, j] *= scale
+                T.copy(scores_max, scores_max_prev)
+                T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+                for i in T.Parallel(h):
+                    scores_scale[i] = T.exp(scores_max_prev[i] - scores_max[i])
+                for i, j in T.Parallel(h, block):
+                    acc_s[i, j] = T.exp(acc_s[i, j] - scores_max[i])
+                T.reduce_sum(acc_s, scores_sum, dim=1)
+                for i in T.Parallel(h):
+                    sum_exp[i] = sum_exp[i] * scores_scale[i] + scores_sum[i]
+                T.copy(acc_s, acc_s_cast)
+                for i, j in T.Parallel(h, d):
+                    acc_o[i, j] *= scores_scale[i]
+                T.gemm(acc_s_cast, kv_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+
+            for i in T.Parallel(h):
+                sum_exp[i] += T.exp(attn_sink[i] - scores_max[i])
+            for i, j in T.Parallel(h, d):
+                acc_o[i, j] /= sum_exp[i]
+            T.copy(acc_o, o_shared)
+            T.copy(o_shared, o[by, bx, :, :])
+
+    return sparse_attn_kernel_
+
+
+def sparse_attn(
+    q: torch.Tensor, kv: torch.Tensor, attn_sink: torch.Tensor, topk_idxs: torch.Tensor, softmax_scale: float
+) -> torch.Tensor:
+    b, s, h, d = q.size()
+    # Pad heads to 16 for kernel efficiency (stripped after)
+    if h < 16:
+        q = torch.cat([q, q.new_zeros(b, s, 16 - h, d)], dim=2)
+        attn_sink = torch.cat([attn_sink, attn_sink.new_zeros(16 - h)])
+    o = torch.empty_like(q)
+    kernel = sparse_attn_kernel(q.size(2), d, softmax_scale)
+    kernel(q, kv, o, attn_sink, topk_idxs)
+    if h < 16:
+        o = o.narrow(2, 0, h).contiguous()
+    return o
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def hc_split_sinkhorn_kernel(hc: int, sinkhorn_iters: int, eps: float):
+    n = T.symbolic("n")
+    mix_hc = (2 + hc) * hc
+    threads = 64
+
+    @T.prim_func
+    def hc_split_sinkhorn_kernel_(
+        mixes: T.Tensor[(n, mix_hc), FP32],
+        hc_scale: T.Tensor[(3,), FP32],
+        hc_base: T.Tensor[(mix_hc,), FP32],
+        pre: T.Tensor[(n, hc), FP32],
+        post: T.Tensor[(n, hc), FP32],
+        comb: T.Tensor[(n, hc, hc), FP32],
+    ):
+        with T.Kernel(n, threads=threads) as i:
+            mixes_shared = T.alloc_shared(mix_hc, FP32)
+            comb_frag = T.alloc_fragment((hc, hc), FP32)
+            T.copy(mixes[i, :], mixes_shared)
+
+            for j in T.Parallel(hc):
+                pre[i, j] = T.sigmoid(mixes_shared[j] * hc_scale[0] + hc_base[j]) + eps
+            for j in T.Parallel(hc):
+                post[i, j] = 2 * T.sigmoid(mixes_shared[j + hc] * hc_scale[1] + hc_base[j + hc])
+            for j, k in T.Parallel(hc, hc):
+                comb_frag[j, k] = mixes_shared[j * hc + k + hc * 2] * hc_scale[2] + hc_base[j * hc + k + hc * 2]
+
+            row_sum = T.alloc_fragment(hc, FP32)
+            col_sum = T.alloc_fragment(hc, FP32)
+
+            # comb = comb.softmax(-1) + eps
+            row_max = T.alloc_fragment(hc, FP32)
+            T.reduce_max(comb_frag, row_max, dim=1)
+            for j, k in T.Parallel(hc, hc):
+                comb_frag[j, k] = T.exp(comb_frag[j, k] - row_max[j])
+            T.reduce_sum(comb_frag, row_sum, dim=1)
+            for j, k in T.Parallel(hc, hc):
+                comb_frag[j, k] = comb_frag[j, k] / row_sum[j] + eps
+
+            # comb = comb / (comb.sum(-2) + eps)
+            T.reduce_sum(comb_frag, col_sum, dim=0)
+            for j, k in T.Parallel(hc, hc):
+                comb_frag[j, k] = comb_frag[j, k] / (col_sum[k] + eps)
+
+            for _ in T.serial(sinkhorn_iters - 1):
+                # comb = comb / (comb.sum(-1) + eps)
+                T.reduce_sum(comb_frag, row_sum, dim=1)
+                for j, k in T.Parallel(hc, hc):
+                    comb_frag[j, k] = comb_frag[j, k] / (row_sum[j] + eps)
+                # comb = comb / (comb.sum(-2) + eps)
+                T.reduce_sum(comb_frag, col_sum, dim=0)
+                for j, k in T.Parallel(hc, hc):
+                    comb_frag[j, k] = comb_frag[j, k] / (col_sum[k] + eps)
+
+            T.copy(comb_frag, comb[i, :, :])
+
+    return hc_split_sinkhorn_kernel_
+
+
+def hc_split_sinkhorn(mixes: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor, hc_mult: int = 4, sinkhorn_iters: int = 20, eps: float = 1e-6):
+    b, s, _ = mixes.size()
+    pre = mixes.new_empty(b, s, hc_mult)
+    post = mixes.new_empty(b, s, hc_mult)
+    comb = mixes.new_empty(b, s, hc_mult, hc_mult)
+    kernel = hc_split_sinkhorn_kernel(hc_mult, sinkhorn_iters, eps)
+    kernel(mixes.view(-1, (2 + hc_mult) * hc_mult), hc_scale, hc_base,
+           pre.view(-1, hc_mult), post.view(-1, hc_mult), comb.view(-1, hc_mult, hc_mult))
+    return pre, post, comb
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def fp4_gemm_kernel(N, K, out_dtype=BF16, accum_dtype=FP32, scale_dtype=FP32):
+    """FP8 act x FP4 weight GEMM kernel.
+
+    C[M, N] = A_fp8[M, K] @ B_fp4[N, K]^T
+
+    Act: 1x128 quant on K (reduce dim), FP8 with configurable scale dtype
+    Weight: 1x32 quant on K (reduce dim), FP4 with E8M0 scale
+
+    B is stored as [N, K//2] in float4_e2m1fn_x2, logical [N, K] in fp4.
+    The FP4 values are packed along the K (last) dimension.
+
+    Strategy: load FP4 sub-blocks of size [block_N, sub_K] (sub_K=32),
+    cast FP4 to FP8 via float, then do FP8xFP8 GEMM.
+    Apply act scale (per 128 on K) and weight scale (per 32 on K) to the accumulator.
+    """
+    M = T.symbolic("M")
+    act_group_size = 128
+    weight_group_size = 32
+    block_M = 32
+    block_N = 128
+    block_K = 32   # matches weight_group_size for simple scale handling
+    n_sub = act_group_size // block_K  # 4 sub-blocks per act scale group
+
+    @T.prim_func
+    def fp4_gemm_kernel_(
+        A: T.Tensor[(M, K), FP8],
+        B: T.Tensor[(N, K), FP4],
+        C: T.Tensor[(M, N), out_dtype],
+        scales_a: T.Tensor[(M, T.ceildiv(K, act_group_size)), scale_dtype],
+        scales_b: T.Tensor[(N, T.ceildiv(K, weight_group_size)), scale_dtype],
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (
+            bx,
+            by,
+        ):
+            A_shared = T.alloc_shared((block_M, block_K), FP8)
+            B_fp4_shared = T.alloc_shared((block_N, block_K), FP4)
+            B_shared = T.alloc_shared((block_N, block_K), FP8)
+            C_shared = T.alloc_shared((block_M, block_N), out_dtype)
+            C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
+            C_local_accum = T.alloc_fragment((block_M, block_N), accum_dtype)
+            scale_a_frag = T.alloc_fragment((block_M,), FP32)
+            scale_b_frag = T.alloc_fragment((block_N,), FP32)
+
+            T.use_swizzle(panel_size=10)
+            T.clear(C_local)
+            T.clear(C_local_accum)
+
+            K_iters = T.ceildiv(K, block_K)
+            for k in T.Pipelined(K_iters, num_stages=2):
+                T.copy(A[by * block_M, k * block_K], A_shared)
+                T.copy(B[bx * block_N, k * block_K], B_fp4_shared)
+                # FP4->FP8 cast must go through FP32 to avoid ambiguous C++ overload
+                for i, j in T.Parallel(block_N, block_K):
+                    B_shared[i, j] = T.Cast(FP8, T.Cast(FP32, B_fp4_shared[i, j]))
+
+                # Weight scale: per 32 on K, indexed by k (each k is one block_K=32)
+                for i in T.Parallel(block_N):
+                    scale_b_frag[i] = T.Cast(FP32, scales_b[bx * block_N + i, k])
+
+                # Act scale: per 128 on K, indexed by k // 4
+                for i in T.Parallel(block_M):
+                    scale_a_frag[i] = T.Cast(FP32, scales_a[by * block_M + i, k // n_sub])
+
+                T.gemm(A_shared, B_shared, C_local, transpose_B=True)
+
+                for i, j in T.Parallel(block_M, block_N):
+                    C_local_accum[i, j] += C_local[i, j] * scale_a_frag[i] * scale_b_frag[j]
+                T.clear(C_local)
+
+            T.copy(C_local_accum, C_shared)
+            T.copy(C_shared, C[by * block_M, bx * block_N])
+
+    return fp4_gemm_kernel_
+
+
+def fp4_gemm(
+    a: torch.Tensor, a_s: torch.Tensor, b: torch.Tensor, b_s: torch.Tensor,
+    scale_dtype: torch.dtype = torch.float32,
+) -> torch.Tensor:
+    """C[M,N] = A_fp8[M,K] @ B_fp4[N,K]^T.
+    A has per-128 act scale; B has per-32 E8M0 weight scale.
+    B is stored as [N, K//2] in float4_e2m1fn_x2 (2 FP4 values per byte, packed along K)."""
+    assert a.is_contiguous() and b.is_contiguous(), "Input tensors must be contiguous"
+    assert a_s.is_contiguous() and b_s.is_contiguous(), (
+        "Scaling factor tensors must be contiguous"
+    )
+    tl_dtype = FE8M0 if scale_dtype == torch.float8_e8m0fnu else FP32
+    K = a.size(-1)
+    M = a.numel() // K
+    N = b.size(0)
+    c = a.new_empty(*a.size()[:-1], N, dtype=torch.get_default_dtype())
+    kernel = fp4_gemm_kernel(N, K, scale_dtype=tl_dtype)
+    kernel(a.view(M, K), b, c.view(M, N), a_s.view(M, -1), b_s)
+    return c

inference/model.py

@@ -0,0 +1,828 @@
+import math
+from dataclasses import dataclass
+from typing import Tuple, Optional, Literal
+from functools import lru_cache
+from contextlib import contextmanager
+
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.distributed as dist
+
+from kernel import act_quant, fp4_act_quant, fp8_gemm, fp4_gemm, sparse_attn, hc_split_sinkhorn
+
+
+world_size = 1
+rank = 0
+block_size = 128
+fp4_block_size = 32
+default_dtype = torch.bfloat16
+scale_fmt = None
+scale_dtype = torch.float32
+
+
+@contextmanager
+def set_dtype(dtype):
+    """Temporarily override torch default dtype, restoring it on exit (even if an exception occurs)."""
+    prev = torch.get_default_dtype()
+    torch.set_default_dtype(dtype)
+    try:
+        yield
+    finally:
+        torch.set_default_dtype(prev)
+
+@dataclass
+class ModelArgs:
+    """Model hyperparameters. Field names match the config JSON keys."""
+    max_batch_size: int = 4
+    max_seq_len: int = 4096
+    dtype: Literal["bf16", "fp8"] = "fp8"
+    scale_fmt: Literal[None, "ue8m0"] = "ue8m0"
+    expert_dtype: Literal[None, "fp4"] = None
+    scale_dtype: Literal["fp32", "fp8"] = "fp8"
+    vocab_size: int = 129280
+    dim: int = 4096
+    moe_inter_dim: int = 4096
+    n_layers: int = 7
+    n_hash_layers: int = 0
+    n_mtp_layers: int = 1
+    n_heads: int = 64
+    # moe
+    n_routed_experts: int = 8
+    n_shared_experts: int = 1
+    n_activated_experts: int = 2
+    score_func: Literal["softmax", "sigmoid", "sqrtsoftplus"] = "sqrtsoftplus"
+    route_scale: float = 1.
+    swiglu_limit: float = 0.
+    # mqa
+    q_lora_rank: int = 1024
+    head_dim: int = 512
+    rope_head_dim: int = 64
+    norm_eps: float = 1e-6
+    o_groups: int = 8
+    o_lora_rank: int = 1024
+    window_size: int = 128
+    compress_ratios: Tuple[int] = (0, 0, 4, 128, 4, 128, 4, 0)
+    # yarn
+    compress_rope_theta: float = 40000.0
+    original_seq_len: int = 0
+    rope_theta: float = 10000.0
+    rope_factor: float = 40
+    beta_fast: int = 32
+    beta_slow: int = 1
+    # index
+    index_n_heads: int = 64
+    index_head_dim: int = 128
+    index_topk: int = 512
+    # hc
+    hc_mult: int = 4
+    hc_sinkhorn_iters: int = 20
+    hc_eps: float = 1e-6
+
+
+class ParallelEmbedding(nn.Module):
+    """Embedding sharded along the vocab dimension. Each rank holds vocab_size // world_size rows.
+    Out-of-range indices are zero-masked before all_reduce to combine partial embeddings."""
+    def __init__(self, vocab_size: int, dim: int):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.dim = dim
+        assert vocab_size % world_size == 0, f"Vocabulary size must be divisible by world size (world_size={world_size})"
+        self.part_vocab_size = (vocab_size // world_size)
+        self.vocab_start_idx = rank * self.part_vocab_size
+        self.vocab_end_idx = self.vocab_start_idx + self.part_vocab_size
+        self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim))
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        if world_size > 1:
+            mask = (x < self.vocab_start_idx) | (x >= self.vocab_end_idx)
+            x = x - self.vocab_start_idx
+            x[mask] = 0
+        y = F.embedding(x, self.weight)
+        if world_size > 1:
+            y[mask] = 0
+            dist.all_reduce(y)
+        return y
+
+
+def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+    """Dispatches to fp4_gemm / fp8_gemm / F.linear based on weight dtype.
+    For quantized weights, x is first quantized to FP8 via act_quant."""
+    assert bias is None
+
+    if weight.dtype == torch.float4_e2m1fn_x2:
+        x, s = act_quant(x, block_size, scale_fmt, scale_dtype)
+        return fp4_gemm(x, s, weight, weight.scale, scale_dtype)
+    elif weight.dtype == torch.float8_e4m3fn:
+        x, s = act_quant(x, block_size, scale_fmt, scale_dtype)
+        return fp8_gemm(x, s, weight, weight.scale, scale_dtype)
+    else:
+        return F.linear(x, weight)
+
+
+class Linear(nn.Module):
+    """Linear layer supporting BF16, FP8, and FP4 weight formats with per-block scaling."""
+
+    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
+        super().__init__()
+        self.in_features = in_features
+        self.out_features = out_features
+        dtype = dtype or default_dtype
+        if dtype == torch.float4_e2m1fn_x2:
+            # FP4: weight is [out, in//2] in float4_e2m1fn_x2, logically [out, in] in fp4
+            # Scale is [out, in//32] in float8_e8m0fnu (1 scale per 32 fp4 elements along K)
+            self.weight = nn.Parameter(torch.empty(out_features, in_features // 2, dtype=torch.float4_e2m1fn_x2))
+            scale_out_features = out_features
+            scale_in_features = in_features // fp4_block_size
+            self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float8_e8m0fnu))
+        elif dtype == torch.float8_e4m3fn:
+            self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype))
+            scale_out_features = (out_features + block_size - 1) // block_size
+            scale_in_features = (in_features + block_size - 1) // block_size
+            self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float8_e8m0fnu))
+        else:
+            self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype))
+            self.register_parameter("scale", None)
+        if bias:
+            self.bias = nn.Parameter(torch.empty(out_features))
+        else:
+            self.register_parameter("bias", None)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return linear(x, self.weight, self.bias)
+
+
+class ColumnParallelLinear(Linear):
+    """Shards output dim across TP ranks. No all-reduce needed on output."""
+    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
+        assert out_features % world_size == 0, f"Output features must be divisible by world size (world_size={world_size})"
+        self.part_out_features = out_features // world_size
+        super().__init__(in_features, self.part_out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        return linear(x, self.weight, self.bias)
+
+
+class RowParallelLinear(Linear):
+    """Shards input dim across TP ranks. All-reduce on output to sum partial results."""
+    def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
+        assert in_features % world_size == 0, f"Input features must be divisible by world size (world_size={world_size})"
+        self.part_in_features = in_features // world_size
+        super().__init__(self.part_in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        y = linear(x, self.weight, None)
+        if world_size > 1:
+            y = y.float()
+            dist.all_reduce(y)
+        if self.bias is not None:
+            y += self.bias
+        return y.type_as(x)
+
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+        # rmsnorm in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for convenient.
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor):
+        dtype = x.dtype
+        x = x.float()
+        var = x.square().mean(-1, keepdim=True)
+        x = x * torch.rsqrt(var + self.eps)
+        return (self.weight * x).to(dtype)
+
+
+@lru_cache(2)
+def precompute_freqs_cis(dim, seqlen, original_seq_len, base, factor, beta_fast, beta_slow) -> torch.Tensor:
+    """Precomputes complex exponentials for rotary embeddings with YaRN scaling.
+    When original_seq_len > 0, applies frequency interpolation with a smooth
+    linear ramp between beta_fast and beta_slow correction ranges."""
+
+    def find_correction_dim(num_rotations, dim, base, max_seq_len):
+        return dim * math.log(max_seq_len / (num_rotations * 2 * math.pi)) / (2 * math.log(base))
+
+    def find_correction_range(low_rot, high_rot, dim, base, max_seq_len):
+        low = math.floor(find_correction_dim(low_rot, dim, base, max_seq_len))
+        high = math.ceil(find_correction_dim(high_rot, dim, base, max_seq_len))
+        return max(low, 0), min(high, dim-1)
+
+    def linear_ramp_factor(min, max, dim):
+        if min == max:
+            max += 0.001
+        linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
+        ramp_func = torch.clamp(linear_func, 0, 1)
+        return ramp_func
+
+    freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
+    if original_seq_len > 0:
+        low, high = find_correction_range(beta_fast, beta_slow, dim, base, original_seq_len)
+        smooth = 1 - linear_ramp_factor(low, high, dim // 2)
+        freqs = freqs / factor * (1 - smooth) + freqs * smooth
+
+    t = torch.arange(seqlen)
+    freqs = torch.outer(t, freqs)
+    freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
+    return freqs_cis
+
+
+def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor, inverse: bool = False) -> torch.Tensor:
+    """Applies rotary positional embeddings in-place. Uses conjugate for inverse (de-rotation)."""
+    y = x
+    x = torch.view_as_complex(x.float().unflatten(-1, (-1, 2)))
+    if inverse:
+        freqs_cis = freqs_cis.conj()
+    if x.ndim == 3:
+        freqs_cis = freqs_cis.view(1, x.size(1), x.size(-1))
+    else:
+        freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
+    x = torch.view_as_real(x * freqs_cis).flatten(-2)
+    y.copy_(x)
+    return y
+
+
+def rotate_activation(x: torch.Tensor) -> torch.Tensor:
+    """Applies randomized Hadamard rotation to spread information across dims before FP8 quant."""
+    assert x.dtype == torch.bfloat16
+    from fast_hadamard_transform import hadamard_transform
+    return hadamard_transform(x, scale=x.size(-1) ** -0.5)
+
+
+@lru_cache(1)
+def get_window_topk_idxs(window_size: int, bsz: int, seqlen: int, start_pos: int):
+    if start_pos >= window_size - 1:
+        start_pos %= window_size
+        matrix = torch.cat([torch.arange(start_pos + 1, window_size),  torch.arange(0, start_pos + 1)], dim=0)
+    elif start_pos > 0:
+        matrix = F.pad(torch.arange(start_pos + 1), (0, window_size - start_pos - 1), value=-1)
+    else:
+        base = torch.arange(seqlen).unsqueeze(1)
+        matrix = (base - window_size + 1).clamp(0) + torch.arange(min(seqlen, window_size))
+        matrix = torch.where(matrix > base, -1, matrix)
+    return matrix.unsqueeze(0).expand(bsz, -1, -1)
+
+
+@lru_cache(2)
+def get_compress_topk_idxs(ratio: int, bsz: int, seqlen: int, start_pos: int, offset: int):
+    if start_pos > 0:
+        matrix = torch.arange(0, (start_pos + 1) // ratio) + offset
+    else:
+        matrix = torch.arange(seqlen // ratio).repeat(seqlen, 1)
+        mask = matrix >= torch.arange(1, seqlen + 1).unsqueeze(1) // ratio
+        matrix = torch.where(mask, -1, matrix + offset)
+    return matrix.unsqueeze(0).expand(bsz, -1, -1)
+
+
+class Compressor(nn.Module):
+    """Compresses KV cache via learned gated pooling over `compress_ratio` consecutive tokens.
+    When overlap=True (ratio==4), uses overlapping windows for smoother compression boundaries."""
+
+    def __init__(self, args: ModelArgs, compress_ratio: int = 4, head_dim: int = 512, rotate: bool = False):
+        super().__init__()
+        self.dim = args.dim
+        self.head_dim = head_dim
+        self.rope_head_dim = args.rope_head_dim
+        self.nope_head_dim = head_dim - args.rope_head_dim
+        self.compress_ratio = compress_ratio
+        self.overlap = compress_ratio == 4
+        self.rotate = rotate
+        coff = 1 + self.overlap
+
+        self.ape = nn.Parameter(torch.empty(compress_ratio, coff * self.head_dim, dtype=torch.float32))
+        # wkv and wgate in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for convenient.
+        # When overlap, the first half of dims is for overlapping compression, second half for normal.
+        self.wkv = Linear(self.dim, coff * self.head_dim, dtype=torch.float32)
+        self.wgate = Linear(self.dim, coff * self.head_dim, dtype=torch.float32)
+        self.norm = RMSNorm(self.head_dim, args.norm_eps)
+        self.kv_cache: torch.Tensor = None  # assigned lazily from Attention.kv_cache
+        # State buffers for decode-phase incremental compression.
+        # With overlap: state[:, :ratio] = overlapping window, state[:, ratio:] = current window.
+        self.register_buffer("kv_state", torch.zeros(args.max_batch_size, coff * compress_ratio, coff * self.head_dim, dtype=torch.float32), persistent=False)
+        self.register_buffer("score_state", torch.full((args.max_batch_size, coff * compress_ratio, coff * self.head_dim), float("-inf"), dtype=torch.float32), persistent=False)
+        self.freqs_cis: torch.Tensor = None
+
+    def overlap_transform(self, tensor: torch.Tensor, value=0):
+        # tensor: [b,s,r,2d]
+        b, s, _, _ = tensor.size()
+        ratio, d = self.compress_ratio, self.head_dim
+        new_tensor = tensor.new_full((b, s, 2 * ratio, d), value)
+        new_tensor[:, :, ratio:] = tensor[:, :, :, d:]
+        new_tensor[:, 1:, :ratio] = tensor[:, :-1, :, :d]
+        return new_tensor
+
+    def forward(self, x: torch.Tensor, start_pos: int):
+        assert self.kv_cache is not None
+        bsz, seqlen, _ = x.size()
+        ratio, overlap, d, rd = self.compress_ratio, self.overlap, self.head_dim, self.rope_head_dim
+        dtype = x.dtype
+        # compression need fp32
+        x = x.float()
+        kv = self.wkv(x)
+        score = self.wgate(x)
+        if start_pos == 0:
+            should_compress = seqlen >= ratio
+            remainder = seqlen % ratio
+            cutoff = seqlen - remainder
+            offset = ratio if overlap else 0
+            if overlap and cutoff >= ratio:
+                self.kv_state[:bsz, :ratio] = kv[:, cutoff-ratio : cutoff]
+                self.score_state[:bsz, :ratio] = score[:, cutoff-ratio : cutoff] + self.ape
+            if remainder > 0:
+                kv, self.kv_state[:bsz, offset : offset+remainder] = kv.split([cutoff, remainder], dim=1)
+                self.score_state[:bsz, offset : offset+remainder] = score[:, cutoff:] + self.ape[:remainder]
+                score = score[:, :cutoff]
+            kv = kv.unflatten(1, (-1, ratio))
+            score = score.unflatten(1, (-1, ratio)) + self.ape
+            if overlap:
+                kv = self.overlap_transform(kv, 0)
+                score = self.overlap_transform(score, float("-inf"))
+            kv = (kv * score.softmax(dim=2)).sum(dim=2)
+        else:
+            should_compress = (start_pos + 1) % self.compress_ratio == 0
+            score += self.ape[start_pos % ratio]
+            if overlap:
+                self.kv_state[:bsz, ratio + start_pos % ratio] = kv.squeeze(1)
+                self.score_state[:bsz, ratio + start_pos % ratio] = score.squeeze(1)
+                if should_compress:
+                    kv_state = torch.cat([self.kv_state[:bsz, :ratio, :d], self.kv_state[:bsz, ratio:, d:]], dim=1)
+                    score_state = torch.cat([self.score_state[:bsz, :ratio, :d], self.score_state[:bsz, ratio:, d:]], dim=1)
+                    kv = (kv_state * score_state.softmax(dim=1)).sum(dim=1, keepdim=True)
+                    self.kv_state[:bsz, :ratio] = self.kv_state[:bsz, ratio:]
+                    self.score_state[:bsz, :ratio] = self.score_state[:bsz, ratio:]
+            else:
+                self.kv_state[:bsz, start_pos % ratio] = kv.squeeze(1)
+                self.score_state[:bsz, start_pos % ratio] = score.squeeze(1)
+                if should_compress:
+                    kv = (self.kv_state[:bsz] * self.score_state[:bsz].softmax(dim=1)).sum(dim=1, keepdim=True)
+        if not should_compress:
+            return
+        kv = self.norm(kv.to(dtype))
+        if start_pos == 0:
+            freqs_cis = self.freqs_cis[:cutoff:ratio]
+        else:
+            freqs_cis = self.freqs_cis[start_pos + 1 - self.compress_ratio].unsqueeze(0)
+        apply_rotary_emb(kv[..., -rd:], freqs_cis)
+        if self.rotate:
+            kv = rotate_activation(kv)
+            fp4_act_quant(kv, fp4_block_size, True)
+        else:
+            act_quant(kv[..., :-rd], 64, scale_fmt, scale_dtype, True)
+        if start_pos == 0:
+            self.kv_cache[:bsz, :seqlen // ratio] = kv
+        else:
+            self.kv_cache[:bsz, start_pos // ratio] = kv.squeeze(1)
+        return kv
+
+
+class Indexer(torch.nn.Module):
+    """Selects top-k compressed KV positions for sparse attention via learned scoring.
+    Has its own Compressor (with Hadamard rotation) to build compressed KV for scoring."""
+
+    def __init__(self, args: ModelArgs, compress_ratio: int = 4):
+        super().__init__()
+        self.dim = args.dim
+        self.n_heads = args.index_n_heads
+        self.n_local_heads = args.index_n_heads // world_size
+        self.head_dim = args.index_head_dim
+        self.rope_head_dim = args.rope_head_dim
+        self.index_topk = args.index_topk
+        self.q_lora_rank = args.q_lora_rank
+        self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.head_dim)
+        self.weights_proj = ColumnParallelLinear(self.dim, self.n_heads, dtype=torch.bfloat16)
+        self.softmax_scale = self.head_dim ** -0.5
+        self.compress_ratio = compress_ratio
+
+        self.compressor = Compressor(args, compress_ratio, self.head_dim, True)
+        self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len // compress_ratio, self.head_dim), persistent=False)
+        self.freqs_cis = None
+
+    def forward(self, x: torch.Tensor, qr: torch.Tensor, start_pos: int, offset: int):
+        bsz, seqlen, _ = x.size()
+        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
+        ratio = self.compress_ratio
+        rd = self.rope_head_dim
+        end_pos = start_pos + seqlen
+        if self.compressor.kv_cache is None:
+            self.compressor.kv_cache = self.kv_cache
+            self.compressor.freqs_cis = self.freqs_cis
+        q = self.wq_b(qr)
+        q = q.unflatten(-1, (self.n_local_heads, self.head_dim))
+        apply_rotary_emb(q[..., -rd:], freqs_cis)
+        q = rotate_activation(q)
+        # use fp4 simulation for q and kv in indexer
+        fp4_act_quant(q, fp4_block_size, True)
+        self.compressor(x, start_pos)
+        weights = self.weights_proj(x) * (self.softmax_scale * self.n_heads ** -0.5)
+        # We performed QAT here, kv could also use fp8 format, though current implementation uses bf16
+        index_score = torch.einsum("bshd,btd->bsht", q, self.kv_cache[:bsz, :end_pos // ratio])
+        index_score = (index_score.relu_() * weights.unsqueeze(-1)).sum(dim=2)
+        if world_size > 1:
+            dist.all_reduce(index_score)
+        if start_pos == 0:
+            mask = torch.arange(seqlen // ratio).repeat(seqlen, 1) >= torch.arange(1, seqlen + 1).unsqueeze(1) // ratio
+            index_score += torch.where(mask, float("-inf"), 0)
+        topk_idxs = index_score.topk(min(self.index_topk, end_pos // ratio), dim=-1)[1]
+        if start_pos == 0:
+            mask = topk_idxs >= torch.arange(1, seqlen + 1).unsqueeze(1) // ratio
+            topk_idxs = torch.where(mask, -1, topk_idxs + offset)
+        else:
+            topk_idxs += offset
+        return topk_idxs
+
+
+class Attention(nn.Module):
+    """Multi-head Latent Attention (MLA) with sliding window + optional KV compression.
+    Uses low-rank Q projection (wq_a -> q_norm -> wq_b) and grouped low-rank O projection."""
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__()
+        self.layer_id = layer_id
+        self.dim = args.dim
+        self.n_heads = args.n_heads
+        self.n_local_heads = args.n_heads // world_size
+        self.q_lora_rank = args.q_lora_rank
+        self.o_lora_rank = args.o_lora_rank
+        self.head_dim = args.head_dim
+        self.rope_head_dim = args.rope_head_dim
+        self.nope_head_dim = args.head_dim - args.rope_head_dim
+        self.n_groups = args.o_groups
+        self.n_local_groups = self.n_groups // world_size
+        self.window_size = args.window_size
+        self.compress_ratio = args.compress_ratios[layer_id]
+        self.eps = args.norm_eps
+
+        self.attn_sink = nn.Parameter(torch.empty(self.n_local_heads, dtype=torch.float32))
+        self.wq_a = Linear(self.dim, self.q_lora_rank)
+        self.q_norm = RMSNorm(self.q_lora_rank, self.eps)
+        self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.head_dim)
+        self.wkv = Linear(self.dim, self.head_dim)
+        self.kv_norm = RMSNorm(self.head_dim, self.eps)
+        self.wo_a = ColumnParallelLinear(self.n_heads * self.head_dim // self.n_groups, self.n_groups * args.o_lora_rank, dtype=torch.bfloat16)
+        self.wo_b = RowParallelLinear(self.n_groups * args.o_lora_rank, self.dim)
+        self.softmax_scale = self.head_dim ** -0.5
+
+        if self.compress_ratio:
+            self.compressor = Compressor(args, self.compress_ratio, self.head_dim)
+            if self.compress_ratio == 4:
+                self.indexer = Indexer(args, self.compress_ratio)
+            else:
+                self.indexer = None
+
+        kv_cache_size = args.window_size + (args.max_seq_len // self.compress_ratio if self.compress_ratio else 0)
+        self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, kv_cache_size, self.head_dim), persistent=False)
+        if self.compress_ratio:
+            original_seq_len, rope_theta = args.original_seq_len, args.compress_rope_theta
+        else:
+            # disable YaRN and use base rope_theta in pure sliding-window attention
+            original_seq_len, rope_theta = 0, args.rope_theta
+        freqs_cis = precompute_freqs_cis(self.rope_head_dim, args.max_seq_len, original_seq_len,
+                                         rope_theta, args.rope_factor, args.beta_fast, args.beta_slow)
+        self.register_buffer("freqs_cis", freqs_cis, persistent=False)
+
+    def forward(self, x: torch.Tensor, start_pos: int):
+        bsz, seqlen, _ = x.size()
+        freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
+        win = self.window_size
+        ratio = self.compress_ratio
+        rd = self.rope_head_dim
+        if self.compress_ratio and self.compressor.kv_cache is None:
+            self.compressor.kv_cache = self.kv_cache[:, win:]
+            self.compressor.freqs_cis = self.freqs_cis
+            if self.indexer is not None:
+                self.indexer.freqs_cis = self.freqs_cis
+        # q
+        qr = q = self.q_norm(self.wq_a(x))
+        q = self.wq_b(q).unflatten(-1, (self.n_local_heads, self.head_dim))
+        q *= torch.rsqrt(q.square().mean(-1, keepdim=True) + self.eps)
+        apply_rotary_emb(q[..., -rd:], freqs_cis)
+
+        # win kv & topk_idxs
+        kv = self.wkv(x)
+        kv = self.kv_norm(kv)
+        apply_rotary_emb(kv[..., -rd:], freqs_cis)
+        # FP8-simulate non-rope dims to match QAT; rope dims stay bf16 for positional precision
+        act_quant(kv[..., :-rd], 64, scale_fmt, scale_dtype, True)
+        topk_idxs = get_window_topk_idxs(win, bsz, seqlen, start_pos)
+        if self.compress_ratio:
+            offset = kv.size(1) if start_pos == 0 else win
+            if self.indexer is not None:
+                compress_topk_idxs = self.indexer(x, qr, start_pos, offset)
+            else:
+                compress_topk_idxs = get_compress_topk_idxs(ratio, bsz, seqlen, start_pos, offset)
+            topk_idxs = torch.cat([topk_idxs, compress_topk_idxs], dim=-1)
+        topk_idxs = topk_idxs.int()
+
+        # compress kv & attn
+        if start_pos == 0:
+            if seqlen <= win:
+                self.kv_cache[:bsz, :seqlen] = kv
+            else:
+                cutoff = seqlen % win
+                self.kv_cache[:bsz, cutoff: win], self.kv_cache[:bsz, :cutoff] = kv[:, -win:].split([win - cutoff, cutoff], dim=1)
+            if self.compress_ratio:
+                if (kv_compress := self.compressor(x, start_pos)) is not None:
+                    kv = torch.cat([kv, kv_compress], dim=1)
+            # We performed QAT here, kv could also use fp8 format, though current implementation uses bf16
+            o = sparse_attn(q, kv, self.attn_sink, topk_idxs, self.softmax_scale)
+        else:
+            self.kv_cache[:bsz, start_pos % win] = kv.squeeze(1)
+            if self.compress_ratio:
+                self.compressor(x, start_pos)
+            o = sparse_attn(q, self.kv_cache[:bsz], self.attn_sink, topk_idxs, self.softmax_scale)
+        apply_rotary_emb(o[..., -rd:], freqs_cis, True)
+
+        # o
+        o = o.view(bsz, seqlen, self.n_local_groups, -1)
+        wo_a = self.wo_a.weight.view(self.n_local_groups, self.o_lora_rank, -1)
+        # NOTE: wo_a is FP8 in checkpoint; could do FP8 einsum here for better perf,
+        # but using BF16 for simplicity.
+        o = torch.einsum("bsgd,grd->bsgr", o, wo_a)
+        x = self.wo_b(o.flatten(2))
+        return x
+
+
+class Gate(nn.Module):
+    """MoE gating: computes expert routing scores and selects top-k experts.
+    Supports hash-based routing (first n_hash_layers) where expert indices are
+    predetermined per token ID, and score-based routing (remaining layers)."""
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__()
+        self.dim = args.dim
+        self.topk = args.n_activated_experts
+        self.score_func = args.score_func
+        self.route_scale = args.route_scale
+        self.hash = layer_id < args.n_hash_layers
+        self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
+        if self.hash:
+            self.tid2eid = nn.Parameter(torch.empty(args.vocab_size, args.n_activated_experts, dtype=torch.int32), requires_grad=False)
+            self.bias = None
+        else:
+            self.bias = nn.Parameter(torch.empty(args.n_routed_experts, dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor, input_ids: Optional[torch.Tensor] = None) -> Tuple[torch.Tensor, torch.Tensor]:
+        scores = linear(x.float(), self.weight.float())
+        if self.score_func == "softmax":
+            scores = scores.softmax(dim=-1)
+        elif self.score_func == "sigmoid":
+            scores = scores.sigmoid()
+        else:
+            scores = F.softplus(scores).sqrt()
+        original_scores = scores
+        # Bias shifts scores for expert selection (topk) but does not affect routing weights.
+        if self.bias is not None:
+            scores = scores + self.bias
+        if self.hash:
+            indices = self.tid2eid[input_ids]
+        else:
+            indices = scores.topk(self.topk, dim=-1)[1]
+        weights = original_scores.gather(1, indices)
+        if self.score_func != "softmax":
+            weights /= weights.sum(dim=-1, keepdim=True)
+        weights *= self.route_scale
+        return weights, indices
+
+
+class Expert(nn.Module):
+    """Single MoE expert: SwiGLU FFN (w1, w2, w3). Computation in float32 for stability."""
+    def __init__(self, dim: int, inter_dim: int, dtype=None, swiglu_limit=0):
+        super().__init__()
+        self.w1 = Linear(dim, inter_dim, dtype=dtype)
+        self.w2 = Linear(inter_dim, dim, dtype=dtype)
+        self.w3 = Linear(dim, inter_dim, dtype=dtype)
+        self.swiglu_limit = swiglu_limit
+
+    def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor] = None) -> torch.Tensor:
+        dtype = x.dtype
+        gate = self.w1(x).float()
+        up = self.w3(x).float()
+        if self.swiglu_limit > 0:
+            up = torch.clamp(up, min=-self.swiglu_limit, max=self.swiglu_limit)
+            gate = torch.clamp(gate, max=self.swiglu_limit)
+        x = F.silu(gate) * up
+        if weights is not None:
+            x = weights * x
+        return self.w2(x.to(dtype))
+
+
+class MoE(nn.Module):
+    """Mixture-of-Experts: gate routes each token to top-k routed experts + 1 shared expert.
+    Experts are sharded across TP ranks; each rank handles n_routed_experts // world_size experts."""
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__()
+        self.layer_id = layer_id
+        self.dim = args.dim
+        assert args.n_routed_experts % world_size == 0, f"Number of experts must be divisible by world size (world_size={world_size})"
+        self.n_routed_experts = args.n_routed_experts
+        self.n_local_experts = args.n_routed_experts // world_size
+        self.n_activated_experts = args.n_activated_experts
+        self.experts_start_idx = rank * self.n_local_experts
+        self.experts_end_idx = self.experts_start_idx + self.n_local_experts
+        self.gate = Gate(layer_id, args)
+        expert_dtype = torch.float4_e2m1fn_x2 if args.expert_dtype == "fp4" else None
+        self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim, dtype=expert_dtype, swiglu_limit=args.swiglu_limit) if self.experts_start_idx <= i < self.experts_end_idx else None
+                                       for i in range(self.n_routed_experts)])
+        assert args.n_shared_experts == 1
+        # no swiglu_limit
+        self.shared_experts = Expert(args.dim, args.moe_inter_dim)
+
+    def forward(self, x: torch.Tensor, input_ids: torch.Tensor) -> torch.Tensor:
+        shape = x.size()
+        x = x.view(-1, self.dim)
+        weights, indices = self.gate(x, input_ids.flatten())
+        y = torch.zeros_like(x, dtype=torch.float32)
+        counts = torch.bincount(indices.flatten(), minlength=self.n_routed_experts).tolist()
+        for i in range(self.experts_start_idx, self.experts_end_idx):
+            if counts[i] == 0:
+                continue
+            expert = self.experts[i]
+            idx, top = torch.where(indices == i)
+            y[idx] += expert(x[idx], weights[idx, top, None])
+        if world_size > 1:
+            dist.all_reduce(y)
+        y += self.shared_experts(x)
+        return y.type_as(x).view(shape)
+
+
+class Block(nn.Module):
+    """Transformer block with Hyper-Connections (HC) mixing.
+    Instead of a simple residual, HC maintains `hc_mult` copies of the hidden state.
+    hc_pre: reduces hc copies -> 1 via learned weighted sum (pre-weights from Sinkhorn).
+    hc_post: expands 1 -> hc copies via learned post-weights + combination matrix."""
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__()
+        self.layer_id = layer_id
+        self.norm_eps = args.norm_eps
+        self.attn = Attention(layer_id, args)
+        self.ffn = MoE(layer_id, args)
+        self.attn_norm = RMSNorm(args.dim, self.norm_eps)
+        self.ffn_norm = RMSNorm(args.dim, self.norm_eps)
+        self.hc_mult = hc_mult = args.hc_mult
+        self.hc_sinkhorn_iters = args.hc_sinkhorn_iters
+        self.hc_eps = args.hc_eps
+        mix_hc = (2 + hc_mult) * hc_mult
+        hc_dim = hc_mult * args.dim
+        with set_dtype(torch.float32):
+            self.hc_attn_fn = nn.Parameter(torch.empty(mix_hc, hc_dim))
+            self.hc_ffn_fn = nn.Parameter(torch.empty(mix_hc, hc_dim))
+            self.hc_attn_base = nn.Parameter(torch.empty(mix_hc))
+            self.hc_ffn_base = nn.Parameter(torch.empty(mix_hc))
+            self.hc_attn_scale = nn.Parameter(torch.empty(3))
+            self.hc_ffn_scale = nn.Parameter(torch.empty(3))
+
+    def hc_pre(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor):
+        # x: [b,s,hc,d], hc_fn: [mix_hc,hc*d], hc_scale: [3], hc_base: [mix_hc], y: [b,s,hc,d]
+        shape, dtype = x.size(), x.dtype
+        x = x.flatten(2).float()
+        rsqrt = torch.rsqrt(x.square().mean(-1, keepdim=True) + self.norm_eps)
+        mixes = F.linear(x, hc_fn) * rsqrt
+        pre, post, comb = hc_split_sinkhorn(mixes, hc_scale, hc_base, self.hc_mult, self.hc_sinkhorn_iters, self.hc_eps)
+        y = torch.sum(pre.unsqueeze(-1) * x.view(shape), dim=2)
+        return y.to(dtype), post, comb
+
+    def hc_post(self, x: torch.Tensor, residual: torch.Tensor, post: torch.Tensor, comb: torch.Tensor):
+        # x: [b,s,d], residual: [b,s,hc,d], post: [b,s,hc], comb: [b,s,hc,hc], y: [b,s,hc,d]
+        y = post.unsqueeze(-1) * x.unsqueeze(-2) + torch.sum(comb.unsqueeze(-1) * residual.unsqueeze(-2), dim=2)
+        return y.type_as(x)
+
+    def forward(self, x: torch.Tensor, start_pos: int, input_ids: Optional[torch.Tensor]) -> torch.Tensor:
+        residual = x
+        x, post, comb = self.hc_pre(x, self.hc_attn_fn, self.hc_attn_scale, self.hc_attn_base)
+        x = self.attn_norm(x)
+        x = self.attn(x, start_pos)
+        x = self.hc_post(x, residual, post, comb)
+
+        residual = x
+        x, post, comb = self.hc_pre(x, self.hc_ffn_fn, self.hc_ffn_scale, self.hc_ffn_base)
+        x = self.ffn_norm(x)
+        x = self.ffn(x, input_ids)
+        x = self.hc_post(x, residual, post, comb)
+        return x
+
+
+class ParallelHead(nn.Module):
+
+    def __init__(self, vocab_size: int, dim: int, norm_eps: float = 1e-6, hc_eps: float = 1e-6):
+        super().__init__()
+        self.vocab_size = vocab_size
+        self.dim = dim
+        self.norm_eps = norm_eps
+        self.hc_eps = hc_eps
+        self.part_vocab_size = (vocab_size // world_size)
+        # lm_head in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for easier computation of logits later.
+        self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim, dtype=torch.float32))
+
+    def get_logits(self, x):
+        return F.linear(x[:, -1].float(), self.weight)
+
+    def forward(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor, norm: RMSNorm):
+        # x: [b,s,hc,d]
+        x = self.hc_head(x, hc_fn, hc_scale, hc_base)
+        logits = self.get_logits(norm(x))
+        if world_size > 1:
+            all_logits = [torch.empty_like(logits) for _ in range(world_size)]
+            dist.all_gather(all_logits, logits)
+            logits = torch.cat(all_logits, dim=-1)
+        return logits
+
+    def hc_head(self, x: torch.Tensor, hc_fn: torch.Tensor, hc_scale: torch.Tensor, hc_base: torch.Tensor):
+        shape, dtype = x.size(), x.dtype
+        x = x.flatten(2).float()
+        rsqrt = torch.rsqrt(x.square().mean(-1, keepdim=True) + self.norm_eps)
+        mixes = F.linear(x, hc_fn) * rsqrt
+        pre = torch.sigmoid(mixes * hc_scale + hc_base) + self.hc_eps
+        y = torch.sum(pre.unsqueeze(-1) * x.view(shape), dim=2)
+        return y.to(dtype)
+
+
+class MTPBlock(Block):
+
+    def __init__(self, layer_id: int, args: ModelArgs):
+        super().__init__(layer_id, args)
+        self.e_proj = Linear(args.dim, args.dim)
+        self.h_proj = Linear(args.dim, args.dim)
+        self.enorm = RMSNorm(args.dim, args.norm_eps)
+        self.hnorm = RMSNorm(args.dim, args.norm_eps)
+        self.norm = RMSNorm(args.dim, args.norm_eps)
+        self.hc_mult = hc_mult = args.hc_mult
+        hc_dim = hc_mult * args.dim
+        with set_dtype(torch.float32):
+            self.hc_head_fn = nn.Parameter(torch.empty(hc_mult, hc_dim))
+            self.hc_head_base = nn.Parameter(torch.empty(hc_mult))
+            self.hc_head_scale = nn.Parameter(torch.empty(1))
+        self.embed: ParallelEmbedding = None
+        self.head: ParallelHead = None
+
+    @torch.inference_mode()
+    def forward(self, x: torch.Tensor, start_pos: int, input_ids: torch.Tensor) -> torch.Tensor:
+        # x: [b,s,hc,d]
+        assert self.embed is not None and self.head is not None
+        e = self.embed(input_ids)
+        e = self.enorm(e)
+        x = self.hnorm(x)
+        x = self.e_proj(e).unsqueeze(2) + self.h_proj(x)
+        x = super().forward(x, start_pos, input_ids)
+        logits = self.head(x, self.hc_head_fn, self.hc_head_scale, self.hc_head_base, self.norm)
+        return logits
+
+
+class Transformer(nn.Module):
+    """Full DeepSeek-V4 model: embed -> HC-expand -> N blocks -> HC-head -> logits.
+    Sets global state (world_size, rank, default_dtype, scale_fmt, scale_dtype) in __init__."""
+    def __init__(self, args: ModelArgs):
+        global world_size, rank, default_dtype, scale_fmt, scale_dtype
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        rank = dist.get_rank() if dist.is_initialized() else 0
+        default_dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
+        scale_fmt = "ue8m0" if args.scale_dtype == "fp8" else args.scale_fmt
+        scale_dtype = torch.float8_e8m0fnu if args.scale_dtype == "fp8" else torch.float32
+        super().__init__()
+        self.max_seq_len = args.max_seq_len
+        self.norm_eps = args.norm_eps
+        self.hc_eps = args.hc_eps
+        self.embed = ParallelEmbedding(args.vocab_size, args.dim)
+        self.layers = torch.nn.ModuleList()
+        for layer_id in range(args.n_layers):
+            self.layers.append(Block(layer_id, args))
+        self.norm = RMSNorm(args.dim, self.norm_eps)
+        self.head = ParallelHead(args.vocab_size, args.dim, self.norm_eps, self.hc_eps)
+        self.mtp = torch.nn.ModuleList()
+        for layer_id in range(args.n_mtp_layers):
+            self.mtp.append(MTPBlock(args.n_layers + layer_id, args))
+            self.mtp[-1].embed = self.embed
+            self.mtp[-1].head = self.head
+        self.hc_mult = hc_mult = args.hc_mult
+        hc_dim = hc_mult * args.dim
+        with set_dtype(torch.float32):
+            self.hc_head_fn = nn.Parameter(torch.empty(hc_mult, hc_dim))
+            self.hc_head_base = nn.Parameter(torch.empty(hc_mult))
+            self.hc_head_scale = nn.Parameter(torch.empty(1))
+
+    @torch.inference_mode()
+    def forward(self, input_ids: torch.Tensor, start_pos: int = 0):
+        h = self.embed(input_ids)
+        # Expand to hc_mult copies for Hyper-Connections
+        h = h.unsqueeze(2).repeat(1, 1, self.hc_mult, 1)
+        for layer in self.layers:
+            h = layer(h, start_pos, input_ids)
+        logits = self.head(h, self.hc_head_fn, self.hc_head_scale, self.hc_head_base, self.norm)
+        return logits
+
+
+if __name__ == "__main__":
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_default_device("cuda")
+    torch.manual_seed(0)
+    args = ModelArgs(n_hash_layers=0)
+    x = torch.randint(0, args.vocab_size, (2, 128))
+    model = Transformer(args)
+
+    print(model(x).size())
+    for i in range(128, 150):
+        print(i, model(x[:, 0:1], i).size())
+
+    h = torch.randn(2, 128, args.hc_mult, args.dim)
+    mtp = model.mtp[0]
+    print(mtp(h, 0, x).size())
+    print(mtp(h[:, 0:1], 1, x[:, 0:1]).size())

inference/requirements.txt

@@ -0,0 +1,5 @@
+torch>=2.10.0
+transformers>=5.0.0
+safetensors>=0.7.0
+fast_hadamard_transform
+tilelang==0.1.8