[Example] Add topk into sparse mla example and append some docs (#901)

* Remove unused `fp8_mqa_logits.py` file and update README.md to reflect new directory structure and file descriptions for deepseek_v32 example. Added sections for architecture overview, Lightning Indexer, Top-k Selector, and Sparse MLA Forward implementations.

* Update linting configurations and improve code formatting in deepseek_v32 example scripts

- Added per-file ignores for the inference directory in `pyproject.toml`.
- Refactored code in `topk_selector.py`, `convert.py`, `generate.py`, `kernel.py`, and `model.py` to enhance readability by adjusting spacing and line breaks.
- Ensured consistent formatting across function definitions and assertions for better clarity.

* Refactor test functions in deepseek_v32 example scripts for improved clarity and consistency

- Updated `fp8_lighting_indexer.py` to define a dedicated test function for the lighting indexer.
- Refactored `sparse_mla_fwd_pipelined.py` and `sparse_mla_fwd.py` to standardize test function parameters and improve readability.
- Enhanced `topk_selector.py` by introducing a test function with parameters for batch size and sequence length.
- Ensured all test functions are invoked correctly in the main execution block.

* Enhance test functions in deepseek_v32 example scripts with CUDA requirements and parameterization

- Added CUDA requirements decorators to `test_example_sparse_mla_fwd` and `test_example_sparse_mla_fwd_pipelined`.
- Parameterized test functions to use specific small shapes for testing, improving test coverage and clarity.

* lint fix

* Update README.md to correct image path for DeepSeek V3.2 architecture diagram

examples/deepseek_v32/README.md

@@ -3,7 +3,166 @@
 ```
 deepseek_v32/
 ├── README.md                           # This file
-├── fp8_mqa_logits.py                   # FP8 Indexer
+├── figures/                            # Figures and diagrams
+├── inference/                          # Inference implementation folder
+├── fp8_lighting_indexer.py             # FP8 lighting indexer
 ├── sparse_mla_fwd.py                   # Sparse MLA forward implementation
 ├── sparse_mla_fwd_pipelined.py         # Pipelined implementation of sparse MLA forward pass
+├── topk_selector.py                    # Top-k selector implementation
 ```
+
+## File Descriptions
+
+### Architecture Overview
+
+![DeepSeek V3.2 Architecture](./figures/v32_arch.png)
+
+The architecture diagram above highlights three key components (shown in green) that correspond to our kernel implementations:
+
+1. **Lightning Indexer** (`fp8_lighting_indexer.py`) - Efficiently indexes and processes sparse attention patterns using FP8 precision
+2. **Top-k Selector** (`topk_selector.py`) - Selects the top-k most relevant tokens for sparse attention computation
+3. **Multi-Query Attention** (`sparse_mla_fwd.py` and `sparse_mla_fwd_pipelined.py`) - Core attention mechanism implementation with sparse MLA (Multi-Latent Attention) forward pass
+
+### Lightning Indexer
+
+Looking at the architecture diagram, the Lightning Indexer sits at the bottom right. It takes the input hidden states and produces compressed representations `{q^A_{t,i}}`, `{k^R_t}`, and `{w^I_{t,j}}`. These FP8-quantized index vectors are what feed into the top-k selector.
+
+The main kernel `mqa_attn_return_logits_kernel` computes similarity scores between query and key indices:
+
+```python
+T.gemm(
+    index_k_shared,
+    index_q_shared,
+    s,
+    transpose_B=True,
+    clear_accum=True,
+    policy=T.GemmWarpPolicy.FullCol,
+)
+```
+
+After the matmul, we apply ReLU and aggregate across heads with learned weights:
+
+```python
+for bn_i, bq_i, h_i in T.Parallel(block_N, block_Q, heads):
+    s_reshaped[bn_i, bq_i, h_i] = (
+        T.max(s[bn_i, bq_i * heads + h_i], 0) * weights[bq_i, h_i]
+    ) * index_k_scale_fragment[bn_i]
+
+T.reduce_sum(s_reshaped, logits, dim=-1, clear=True)
+```
+
+The result is a `[seq_len, seq_len_kv]` logits matrix. For long sequences, the kernel uses per-token bounds (`CuSeqLenKS`, `CuSeqLenKE`) to skip irrelevant KV positions:
+
+```python
+for bq_i in T.serial(block_Q):
+    cu_k_s_min[0] = T.min(cu_k_s_min[0], T.min(CuSeqLenKS[seq_len_i + bq_i], seq_len_kv))
+for bq_i in T.serial(block_Q):
+    cu_k_e_max[0] = T.max(cu_k_e_max[0], T.min(CuSeqLenKE[seq_len_i + bq_i], seq_len_kv))
+```
+
+The pipelined loop then only processes keys in the `[cu_k_s_min, cu_k_e_max)` range, which is crucial for handling variable-length sequences in distributed training.
+
+### Top-k Selector
+
+The Top-k Selector takes the logits matrix from the indexer and picks the top-k indices for each query. In the architecture diagram, this sits between the Lightning Indexer and the Multi-Query Attention block. The output indices tell the attention layer which KV tokens to actually load and process.
+
+The implementation uses a radix-sort-based approach that processes floats as unsigned integers. Stage 1 does a quick 8-bit pass over the whole sequence:
+
+```python
+for s in T.serial(T.ceildiv(seq_len, BLOCK_SIZE)):
+    input_idx = s*BLOCK_SIZE+tx
+    if input_idx < l_end_idx and input_idx >= l_start_idx and input_idx < seq_len:
+        inval_int16 = convert_to_uint16(input[bx, input_idx])
+        T.atomic_add(s_histogram[inval_int16], 1)
+```
+
+The `convert_to_uint16` function maps floats to uint16 such that larger floats map to larger integers. After building a histogram and doing a cumulative sum, we find the threshold bin:
+
+```python
+if s_histogram[tx] > l_new_topk and s_histogram[tx + 1] <= l_new_topk:
+    s_threshold_bin_id[0] = tx
+```
+
+Elements above the threshold go directly to the output. Elements in the threshold bin get collected for further processing:
+
+```python
+if l_bin_id32 > l_threshold_bin_id:
+    pos = T.atomic_add(s_histogram[l_bin_id32+1], 1, return_prev=True)
+    index[bx, pos] = input_idx
+elif l_bin_id32 == l_threshold_bin_id and l_new_topk > 0:
+    pos = T.atomic_add(s_num_input[0], 1, return_prev=True)
+    s_input_idx[0, pos] = input_idx
+```
+
+Stage 2 refines the threshold bin with up to 4 rounds of 8-bit radix sort, processing progressively higher bits. This gives exact top-k selection without sorting the entire sequence.
+
+### Sparse MLA Forward
+
+The Sparse MLA kernel is where the actual attention computation happens. In the architecture diagram, this is the large "Multi-Query Attention (Core Attention)" block at the top. It takes the selected top-k indices and computes attention only over those tokens.
+
+Turning dense MLA into sparse MLA requires surprisingly few changes - essentially just modifying how we iterate and load KV tokens. The key difference from dense MLA (see `../deepseek_mla/example_mla_decode.py`) is the iteration pattern. Dense MLA iterates over all KV positions:
+
+```python
+# Dense MLA: iterate over full sequence
+loop_range = T.ceildiv(seqlen_kv, block_N)
+for k in T.Pipelined(loop_range, num_stages=2):
+    T.copy(KV[bid, k * block_N:(k + 1) * block_N, cur_kv_head, :], KV_shared)
+    # ... compute attention over this block
+```
+
+Sparse MLA only loads KV positions selected by the top-k selector:
+
+```python
+# Sparse MLA: iterate over selected indices only
+for i_i in T.Pipelined(NI, num_stages=num_stages):
+    for bi_i, d_i in T.Parallel(BI, D):
+        KV_shared[bi_i, d_i] = KV[b_i, Indices[b_i, s_i, g_i, i_i * BI + bi_i], g_i, d_i]
+    # ... compute attention over selected tokens
+```
+
+This reduces compute from O(seq_len * seq_len_kv) to O(seq_len * topk). The causal mask is enforced by checking whether each index position is valid:
+
+```python
+for bi_i in T.Parallel(BI):
+    mask[bi_i] = Indices[b_i, s_i, g_i, i_i * BI + bi_i] <= max_kv_i
+```
+
+Beyond this sparse indexing, the rest of the attention computation (online softmax, output accumulation) follows the same pattern as dense MLA.
+
+### Sparse MLA Forward (Pipelined)
+
+The pipelined version (`sparse_mla_fwd_pipelined.py`) is a manual pipeline implementation designed to match the schedule of [FlashMLA](https://github.com/deepseek-ai/FlashMLA/blob/main/csrc/sm90/prefill/sparse/fwd.cu). It achieves close to 600 TFlops on H800 SXM by carefully orchestrating memory and compute pipelines.
+
+The key difference is splitting the warp groups into specialized roles:
+
+```python
+if tx < 128:
+    # Consumer 0: computes left half of output (D//2 dimensions)
+    # Handles QK matmul, softmax, and PV for left half
+
+elif tx >= 128 and tx < 256:
+    # Consumer 1: computes right half of output (D//2 dimensions)
+    # Only does PV matmul for right half
+
+elif tx >= 256:
+    # Producer: loads KV data from global memory
+    # Uses async copy with barriers to feed consumers
+```
+
+The producer thread group (tx >= 256) uses double buffering with barriers to keep consumers fed:
+
+```python
+# Producer alternates between two buffers
+for i_i in T.serial(T.ceildiv(NI, 2)):
+    # Buffer 0
+    T.barrier_wait(bar_k_0_free[0], ((i_i & 1) ^ 1))
+    # ... load KV into buffer 0
+    T.cp_async_barrier_noinc(bar_k_0_ready[0])
+
+    # Buffer 1
+    T.barrier_wait(bar_k_1_free[0], ((i_i & 1) ^ 1))
+    # ... load KV into buffer 1
+    T.cp_async_barrier_noinc(bar_k_1_ready[0])
+```
+
+Consumer threads wait on barriers and process buffers as they become ready. This manual orchestration hides memory latency behind compute, which is why it outperforms the simpler auto-pipelined version. The output dimension is also split in half so that the two consumer groups can work in parallel on different parts of the matmul.

examples/deepseek_v32/fp8_mqa_logits.py → examples/deepseek_v32/fp8_lighting_indexer.py

@@ -258,10 +258,7 @@ def ref_fp8_mqa_logits(q: torch.Tensor, kv: torch.Tensor, weights: torch.Tensor,
     cost = mask.sum()
     return logits, cost
 
-
-if __name__ == "__main__":
-    torch.manual_seed(0)
-    S, SKV, H, HKV, D, kv_stride = 4096, 8192, 32, 1, 64, 1
+def test_fp8_lighting_indexer(S=4096, SKV=8192, H=32, HKV=1, D=64, kv_stride=1):
     q = torch.randn(S, H, D, device="cuda", dtype=torch.bfloat16).to(torch.bfloat16)
     kv = torch.randn(SKV, D, device="cuda", dtype=torch.bfloat16).to(torch.bfloat16)
     weights = torch.randn(S, H, device="cuda", dtype=torch.float32)
@@ -304,3 +301,6 @@ if __name__ == "__main__":
     logits_tflops = logits_flops / (logits_ms * 1e-3) / 1e12
     print(f"logits_tflops: {logits_tflops}, logits_ms: {logits_ms}")
     print(f"cost_ref: {cost_ref}")
+
+if __name__ == "__main__":
+    test_fp8_lighting_indexer()

examples/deepseek_v32/inference/README.md

+# DeepSeek V3.2
+
+First convert huggingface model weights to the the format required by our inference demo. Set `MP` to match your available GPU count:
+```bash
+cd inference
+export EXPERTS=256
+python convert.py --hf-ckpt-path ${HF_CKPT_PATH} --save-path ${SAVE_PATH} --n-experts ${EXPERTS} --model-parallel ${MP}
+```
+
+Launch the interactive chat interface and start exploring DeepSeek's capabilities:
+```bash
+export CONFIG=config_671B_v3.2.json
+torchrun --nproc-per-node ${MP} generate.py --ckpt-path ${SAVE_PATH} --config ${CONFIG} --interactive
+```
\ No newline at end of file

examples/deepseek_v32/inference/config_671B_v3.2.json

+{
+    "vocab_size": 129280,
+    "dim": 7168,
+    "inter_dim": 18432,
+    "moe_inter_dim": 2048,
+    "n_layers": 61,
+    "n_dense_layers": 3,
+    "n_heads": 128,
+    "n_routed_experts": 256,
+    "n_shared_experts": 1,
+    "n_activated_experts": 8,
+    "n_expert_groups": 8,
+    "n_limited_groups": 4,
+    "route_scale": 2.5,
+    "score_func": "sigmoid",
+    "q_lora_rank": 1536,
+    "kv_lora_rank": 512,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "v_head_dim": 128,
+    "dtype": "fp8",
+    "scale_fmt": "ue8m0",
+    "index_n_heads": 64,
+    "index_head_dim": 128,
+    "index_topk": 2048
+}
\ No newline at end of file

examples/deepseek_v32/inference/convert.py

+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
+
+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": ("gate", None),
+    "gate_proj": ("w1", 0),
+    "down_proj": ("w2", 1),
+    "up_proj": ("w3", 0),
+    "norm": ("norm", None),
+    "lm_head": ("head", 0),
+    "scale": ("scale", None),
+    "wq_b": ("wq_b", None),
+    "wk": ("wk", None),
+    "k_norm": ("k_norm", None),
+    "weights_proj": ("weights_proj", None),
+}
+
+
+def main(hf_ckpt_path, save_path, n_experts, mp):
+    """
+    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():
+                if "model.layers.61" in name:
+                    continue
+                param: torch.Tensor = f.get_tensor(name)
+                if name.startswith("model."):
+                    name = name[len("model."):]
+                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")
+                key = name.split(".")[-2]
+                assert key in mapping, f"Key {key} not found in mapping"
+                new_key, dim = mapping[key]
+                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):
+        save_file(state_dicts[i], os.path.join(save_path, f"model{i}-mp{mp}.safetensors"))
+
+    for file_path in glob(os.path.join(hf_ckpt_path, "*token*")):
+        new_file_path = os.path.join(save_path, os.path.basename(file_path))
+        shutil.copyfile(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)
+    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)

examples/deepseek_v32/inference/generate.py

+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
+
+
+def sample(logits, temperature: float = 1.0):
+    """
+    Samples a token from the logits using temperature scaling.
+
+    Args:
+        logits (torch.Tensor): The logits tensor for token predictions.
+        temperature (float, optional): Temperature for scaling logits. Defaults to 1.0.
+
+    Returns:
+        torch.Tensor: The sampled token.
+    """
+    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]]:
+    """
+    Generates new tokens based on the given prompt tokens using the specified model.
+
+    Args:
+        model (Transformer): The transformer model used for token generation.
+        prompt_tokens (List[List[int]]): A list of lists containing the prompt tokens for each sequence.
+        max_new_tokens (int): The maximum number of new tokens to generate.
+        eos_id (int): The end-of-sequence token ID.
+        temperature (float, optional): The temperature value for sampling. Defaults to 1.0.
+
+    Returns:
+        List[List[int]]: A list of lists containing the generated tokens for each sequence.
+    """
+    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, device="cuda")
+    for i, t in enumerate(prompt_tokens):
+        tokens[i, :len(t)] = torch.tensor(t, dtype=torch.long, device="cuda")
+    prev_pos = 0
+    finished = torch.tensor([False] * len(prompt_tokens), device="cuda")
+    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)]
+        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:
+    """
+    Main function to load the model and perform interactive or batch text generation.
+
+    Args:
+        ckpt_path (str): Path to the model checkpoint directory.
+        config (str): Path to the model configuration file.
+        input_file (str, optional): Path to a file containing input prompts. Defaults to "".
+        interactive (bool, optional): Whether to run in interactive mode. Defaults to True.
+        max_new_tokens (int, optional): Maximum number of new tokens to generate. Defaults to 100.
+        temperature (float, optional): Temperature for sampling. Defaults to 1.0.
+    """
+    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.set_default_dtype(torch.bfloat16)
+    torch.set_num_threads(8)
+    torch.manual_seed(33377335)
+    with open(config) as f:
+        args = ModelArgs(**json.load(f))
+    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"))
+    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.apply_chat_template(messages, add_generation_prompt=True)
+            completion_tokens = generate(model, [prompt_tokens], max_new_tokens,
+                                         tokenizer.eos_token_id, temperature)
+            completion = tokenizer.decode(completion_tokens[0], skip_special_tokens=True)
+            print(completion)
+            messages.append({"role": "assistant", "content": completion})
+    else:
+        with open(input_file) as f:
+            prompts = f.read().split("\n\n")
+        assert len(
+            prompts
+        ) <= args.max_batch_size, f"Number of prompts exceeds maximum batch size ({args.max_batch_size})"
+        prompt_tokens = [
+            tokenizer.apply_chat_template([{
+                "role": "user",
+                "content": prompt
+            }],
+                                          add_generation_prompt=True) for prompt in prompts
+        ]
+        completion_tokens = generate(model, prompt_tokens, max_new_tokens, tokenizer.eos_token_id,
+                                     temperature)
+        completions = tokenizer.batch_decode(completion_tokens, skip_special_tokens=True)
+        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__":
+    """
+    Command-line interface for distributed text generation.
+
+    Arguments:
+        --ckpt-path (str): Path to the model checkpoint directory.
+        --config (str): Path to the model configuration file.
+        --input-file (str, optional): File containing prompts for batch processing.
+        --interactive (bool, optional): Enable interactive mode for generating text.
+        --max-new-tokens (int, optional): Maximum number of new tokens to generate. Defaults to 200.
+        --temperature (float, optional): Temperature for sampling. Defaults to 0.2.
+
+    Raises:
+        AssertionError: If neither input-file nor interactive mode is specified.
+    """
+    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=200)
+    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)

examples/deepseek_v32/inference/kernel.py

+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,
+    tilelang.PassConfigKey.TL_DISABLE_FAST_MATH: True,
+}
+
+FP8 = "float8_e4m3"
+BF16 = "bfloat16"
+FP32 = "float32"
+
+
+def fast_log2_ceil(x):
+    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):
+    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, in_dtype=BF16, out_dtype=FP8, scale_dtype=FP32, round_scale=False):
+    M = T.symbolic("M")
+    fp8_min = -448.0
+    fp8_max = 448.0
+    fp8_max_inv = 1 / fp8_max
+    num_stages = 0 if round_scale else 2
+    blk_m = 32
+    group_size = 128
+
+    @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,), scale_dtype)
+            s_local = T.alloc_fragment((blk_m,), scale_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
+                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] = 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) -> Tuple[torch.Tensor, torch.Tensor]:
+    """
+    Quantizes the input tensor `x` using block-wise quantization.
+
+    Args:
+        x (torch.Tensor): The input tensor to be quantized. Must be contiguous and its last dimension size must be divisible by `block_size`.
+        block_size (int, optional): The size of the blocks to be used for quantization. Default is 128.
+        scale_fmt (Optional[str], optional): The format of the scale. Default is None.
+    Returns:
+        Tuple[torch.Tensor, torch.Tensor]: A tuple containing:
+            - The quantized tensor with dtype `torch.float8_e4m3fn`.
+            - A tensor of scaling factors with dtype `torch.float32`.
+    """
+    assert x.is_contiguous(), "Input tensor must be contiguous"
+    assert x.size(-1) % block_size == 0, (
+        f"Last dimension size must be divisible by block_size (block_size={block_size})")
+    N = x.size(-1)
+    y = torch.empty_like(x, dtype=torch.float8_e4m3fn)
+    s = x.new_empty(*x.size()[:-1], N // block_size, dtype=torch.float32)
+    kernel = act_quant_kernel(N, round_scale=scale_fmt is not None)
+    kernel(x.view(-1, N), y.view(-1, N), s.view(-1, N // block_size))
+    return y, s
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def fp8_gemm_kernel(N, K, out_dtype=BF16, accum_dtype="float32"):
+    assert out_dtype in [BF16, "float32"]
+
+    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)), FP32],
+        scales_b: T.Tensor[(T.ceildiv(N, group_size), T.ceildiv(K, group_size)), FP32],
+    ):
+        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):
+                # Load A into shared memory
+                T.copy(A[by * block_M, k * block_K], A_shared)
+                # Load B into shared memory
+                T.copy(B[bx * block_N, k * block_K], B_shared)
+                # Load scale into shared memory
+                Scale_B = scales_b[bx * block_N // group_size, k]
+                for i in T.Parallel(block_M):
+                    Scale_C_shared[i] = scales_a[by * block_M + i, k] * Scale_B
+
+                T.gemm(A_shared, B_shared, C_local, transpose_B=True)
+                # Promote to enable 2xAcc
+                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)
+            # TMA store
+            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) -> torch.Tensor:
+    """
+    Perform a matrix multiplication using FP8 precision.
+
+    Args:
+        a (torch.Tensor): The first input matrix, must be contiguous.
+        a_s (torch.Tensor): The scaling factor for the first input matrix, must be contiguous.
+        b (torch.Tensor): The second input matrix, must be contiguous.
+        b_s (torch.Tensor): The scaling factor for the second input matrix, must be contiguous.
+
+    Returns:
+        torch.Tensor: The result of the matrix multiplication.
+    """
+    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")
+    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)
+    kernel(a.view(M, K), b, c.view(M, N), a_s.view(M, -1), b_s)
+    return c
+
+
+@tilelang.jit(out_idx=[4], pass_configs=pass_configs)
+def fp8_index_kernel(h: int, d: int):
+    b = T.symbolic("b")
+    m = T.symbolic("m")
+    n = T.symbolic("n")
+
+    blk_n1 = 512
+    blk_n2 = 128
+
+    @T.prim_func
+    def fp8_index_kernel_(
+        q: T.Tensor[(b, m, h, d), FP8],
+        q_s: T.Tensor[(b, m, h), FP32],
+        k: T.Tensor[(b, n, d), FP8],
+        k_s: T.Tensor[(b, n), FP32],
+        o: T.Tensor[(b, m, n), FP32],
+    ) -> None:
+        with T.Kernel(b, m, T.ceildiv(n, blk_n1)) as (i_b, i_m, i1_n):
+            q_smem = T.alloc_shared((h, d), FP8)
+            T.copy(q[i_b, i_m, 0, 0], q_smem)
+
+            q_s_frag = T.alloc_fragment(h, FP32)
+            T.copy(q_s[i_b, i_m, 0], q_s_frag)
+
+            for i2_n in T.Pipelined(blk_n1 // blk_n2, num_stages=2):
+                k_smem = T.alloc_shared((blk_n2, d), FP8)
+                T.copy(k[i_b, i1_n * blk_n1 + i2_n * blk_n2, 0], k_smem)
+
+                k_s_frag = T.alloc_fragment(blk_n2, FP32)
+                T.copy(k_s[i_b, i1_n * blk_n1 + i2_n * blk_n2], k_s_frag)
+
+                logits = T.alloc_fragment((blk_n2, h), FP32)
+                T.gemm(
+                    k_smem,
+                    q_smem,
+                    logits,
+                    transpose_A=False,
+                    transpose_B=True,
+                    clear_accum=True,
+                )
+
+                for i_h, i3_n in T.Parallel(h, blk_n2):
+                    logits[i3_n, i_h] = T.max(logits[i3_n, i_h], 0) * q_s_frag[i_h]
+
+                logits_sum = T.alloc_fragment(blk_n2, FP32)
+                T.reduce_sum(logits, logits_sum, dim=1)
+
+                for i3_n in T.Parallel(blk_n2):
+                    logits_sum[i3_n] *= k_s_frag[i3_n]
+
+                T.copy(logits_sum, o[i_b, i_m, i1_n * blk_n1 + i2_n * blk_n2])
+
+    return fp8_index_kernel_
+
+
+def fp8_index(
+    q: torch.Tensor,
+    q_s: torch.Tensor,
+    k: torch.Tensor,
+    k_s: torch.Tensor,
+) -> torch.Tensor:
+    """
+    Perform index score using FP8 precision.
+
+    Args:
+        q (torch.Tensor): The Q tensor, must be contiguous.
+        q_s (torch.Tensor): The scaling factor for Q (float), must be contiguous.
+        k (torch.Tensor): The K tensor, must be contiguous.
+        k_s (torch.Tensor): The scaling factor for K (e8m0 here), must be contiguous.
+
+        fp8 q @ fp8 k -> fp32 logits
+        relu(fp32 logits) * q_s (weights) -> fp32 logits
+        fp32 logits -> fp32 logits_sum
+        fp32 logits_sum * k_s (e8m0) -> fp32 index_score
+    """
+    return fp8_index_kernel(q.shape[2], q.shape[3])(q, q_s, k, k_s)

examples/deepseek_v32/inference/model.py

+import math
+from dataclasses import dataclass
+from typing import Tuple, Optional, Literal
+
+from einops import rearrange
+import torch
+from torch import nn
+import torch.nn.functional as F
+import torch.distributed as dist
+
+from kernel import act_quant, fp8_gemm, fp8_index
+
+world_size = 1
+rank = 0
+block_size = 128
+
+
+@dataclass
+class ModelArgs:
+    """
+    Data class for defining model arguments and hyperparameters.
+
+    Attributes:
+        max_batch_size (int): Maximum batch size.
+        max_seq_len (int): Maximum sequence length.
+        dtype (Literal["bf16", "fp8"]): Data type for computations.
+        scale_fmt (Optional[str]): Format for quantization scale.
+        vocab_size (int): Vocabulary size.
+        dim (int): Model dimension.
+        inter_dim (int): Intermediate dimension for MLP layers.
+        moe_inter_dim (int): Intermediate dimension for MoE layers.
+        n_layers (int): Number of transformer layers.
+        n_dense_layers (int): Number of dense layers in the model.
+        n_heads (int): Number of attention heads.
+        n_routed_experts (int): Number of routed experts for MoE layers.
+        n_shared_experts (int): Number of shared experts for MoE layers.
+        n_activated_experts (int): Number of activated experts in MoE layers.
+        n_expert_groups (int): Number of expert groups.
+        n_limited_groups (int): Number of limited groups for MoE routing.
+        score_func (Literal["softmax", "sigmoid"]): Scoring function for MoE routing.
+        route_scale (float): Scaling factor for routing scores.
+        q_lora_rank (int): LoRA rank for query projections.
+        kv_lora_rank (int): LoRA rank for key-value projections.
+        qk_nope_head_dim (int): Dimension for query-key projections without positional embeddings.
+        qk_rope_head_dim (int): Dimension for query-key projections with rotary embeddings.
+        v_head_dim (int): Dimension for value projections.
+        original_seq_len (int): Original sequence length.
+        rope_theta (float): Base for rotary positional encoding.
+        rope_factor (float): Scaling factor for extended sequence lengths.
+        beta_fast (int): Fast beta correction factor.
+        beta_slow (int): Slow beta correction factor.
+        mscale (float): Scaling factor for extended attention.
+        index_head_dim (int): Dimension for index head.
+        index_topk (int): Top-k for index head.
+    """
+    max_batch_size: int = 8
+    max_seq_len: int = 4096 * 4
+    dtype: Literal["bf16", "fp8"] = "bf16"
+    scale_fmt: Optional[str] = None
+    vocab_size: int = 102400
+    dim: int = 2048
+    inter_dim: int = 10944
+    moe_inter_dim: int = 1408
+    n_layers: int = 27
+    n_dense_layers: int = 1
+    n_heads: int = 16
+    # moe
+    n_routed_experts: int = 64
+    n_shared_experts: int = 2
+    n_activated_experts: int = 6
+    n_expert_groups: int = 1
+    n_limited_groups: int = 1
+    score_func: Literal["softmax", "sigmoid"] = "softmax"
+    route_scale: float = 1.
+    # mla
+    q_lora_rank: int = 0
+    kv_lora_rank: int = 512
+    qk_nope_head_dim: int = 128
+    qk_rope_head_dim: int = 64
+    v_head_dim: int = 128
+    # yarn
+    original_seq_len: int = 4096
+    rope_theta: float = 10000.0
+    rope_factor: float = 40
+    beta_fast: int = 32
+    beta_slow: int = 1
+    mscale: float = 1.
+    # index
+    index_n_heads: int = 64
+    index_head_dim: int = 128
+    index_topk: int = 2048
+
+
+class ParallelEmbedding(nn.Module):
+    """
+    Embedding layer with parallelism support across distributed processes.
+
+    Args:
+        vocab_size (int): Vocabulary size.
+        dim (int): Embedding dimension.
+    """
+
+    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:
+        """
+        Forward pass for parallel embedding layer.
+
+        Args:
+            x (torch.Tensor): Input tensor containing token indices.
+
+        Returns:
+            torch.Tensor: Embedded representations.
+
+        Raises:
+            ValueError: If `world_size` is not defined.
+        """
+        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,
+           scale_fmt: Optional[str] = None) -> torch.Tensor:
+    """
+    Applies a linear transformation to the incoming data: y = xA^T + b.
+    This function supports specialized implementations based on quantization
+    and tensor formats.
+
+    Args:
+        x (torch.Tensor): The input tensor.
+        weight (torch.Tensor): The weight tensor. It may be quantized and
+            requires dequantization for certain cases.
+        bias (Optional[torch.Tensor]): The bias tensor to be added. Default is None.
+        scale_fmt (Optional[str]): The format of scaling factors.
+
+    Returns:
+        torch.Tensor: The result of the linear transformation, which may involve
+        quantization-aware computations depending on the input parameters.
+
+    Notes:
+        - If `weight` is quantized (e.g., `element_size() == 1`), a dequantized version
+          is used for computation.
+        - For other cases, the function applies quantization to `x` and uses `fp8_gemm` for computation.
+    """
+    assert bias is None
+
+    if weight.dtype != torch.float8_e4m3fn:
+        return F.linear(x, weight)
+    else:
+        x, scale = act_quant(x, block_size, scale_fmt)
+        return fp8_gemm(x, scale, weight, weight.scale)
+
+
+class Linear(nn.Module):
+    """
+    Custom linear layer with support for quantized weights and optional bias.
+
+    Args:
+        in_features (int): Number of input features.
+        out_features (int): Number of output features.
+        bias (bool): Whether to include a bias term. Defaults to False.
+        dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
+    """
+    dtype = torch.bfloat16
+    scale_fmt: Optional[str] = None
+
+    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
+        self.weight = nn.Parameter(
+            torch.empty(out_features, in_features, dtype=dtype or Linear.dtype))
+        if self.weight.element_size() == 1:
+            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.float32))
+        else:
+            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:
+        """
+        Forward pass for the custom linear layer.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Transformed tensor after linear computation.
+        """
+        return linear(x, self.weight, self.bias, self.scale_fmt)
+
+
+class ColumnParallelLinear(Linear):
+    """
+    Linear layer with column parallelism, splitting output features across distributed processes.
+
+    Args:
+        in_features (int): Number of input features.
+        out_features (int): Total number of output features.
+        bias (bool): Whether to include a bias term. Defaults to False.
+        dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
+    """
+
+    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:
+        """
+        Forward pass for column parallel linear layer.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Transformed tensor with column-parallel computation.
+        """
+        y = linear(x, self.weight, self.bias, self.scale_fmt)
+        return y
+
+
+class RowParallelLinear(Linear):
+    """
+    Linear layer with row parallelism, splitting input features across distributed processes.
+
+    Args:
+        in_features (int): Total number of input features.
+        out_features (int): Number of output features.
+        bias (bool): Whether to include a bias term. Defaults to False.
+        dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
+    """
+
+    def __init__(self,
+                 in_features: int,
+                 out_features: int,
+                 bias: bool = False,
+                 reduce_output=True,
+                 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
+        self.reduce_output = reduce_output
+        super().__init__(self.part_in_features, out_features, bias, dtype)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for row parallel linear layer.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Transformed tensor with row-parallel computation.
+        """
+        y = linear(x, self.weight, None, self.scale_fmt)
+        if self.reduce_output and 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):
+    """
+    Root Mean Square Layer Normalization (RMSNorm).
+
+    Args:
+        dim (int): Dimension of the input tensor.
+        eps (float): Epsilon value for numerical stability. Defaults to 1e-6.
+    """
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor, residual: Optional[torch.Tensor] = None):
+        """
+        Forward pass for RMSNorm.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Normalized tensor with the same shape as input.
+        """
+        dtype = x.dtype
+        if residual is None:
+            x = x.float()
+            var = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(var + self.eps)
+            return (self.weight * x).to(dtype)
+        else:
+            x = residual = x.float() + residual.float()
+            var = x.pow(2).mean(-1, keepdim=True)
+            x = x * torch.rsqrt(var + self.eps)
+            return (self.weight * x).to(dtype), residual.to(dtype)
+
+
+class LayerNorm(nn.Module):
+    """
+    Layer Normalization.
+    """
+
+    def __init__(self, dim: int, eps: float = 1e-6):
+        super().__init__()
+        self.dim = dim
+        self.eps = eps
+        self.weight = nn.Parameter(torch.ones(dim, dtype=torch.float32))
+        self.bias = nn.Parameter(torch.zeros(dim, dtype=torch.float32))
+
+    def forward(self, x: torch.Tensor):
+        return F.layer_norm(x.float(), (self.dim,), self.weight, self.bias, self.eps).type_as(x)
+
+
+def precompute_freqs_cis(args: ModelArgs) -> torch.Tensor:
+    """
+    Precomputes frequency-based complex exponential values for rotary positional embeddings.
+
+    Args:
+        args (ModelArgs): Model arguments containing positional embedding parameters.
+
+    Returns:
+        torch.Tensor: Precomputed complex exponential values for positional embeddings.
+    """
+    dim = args.qk_rope_head_dim
+    seqlen = args.max_seq_len
+    beta_fast = args.beta_fast
+    beta_slow = args.beta_slow
+    base = args.rope_theta
+    factor = args.rope_factor
+
+    def find_correction_dim(num_rotations, dim, base, max_seq_len):
+        """
+        Computes the correction dimension for a given number of rotations in the rotary positional embedding.
+
+        Args:
+            num_rotations (float): Number of rotations to compute the correction for.
+            dim (int): Dimensionality of the embedding space.
+            base (float): Base value for the exponential computation.
+            max_seq_len (int): Maximum sequence length.
+
+        Returns:
+            float: The correction dimension based on the input parameters.
+        """
+        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):
+        """
+        Computes the range of correction dimensions for rotary positional embeddings.
+
+        Args:
+            low_rot (float): Lower bound for the number of rotations.
+            high_rot (float): Upper bound for the number of rotations.
+            dim (int): Dimensionality of the embedding space.
+            base (float): Base value for the exponential computation.
+            max_seq_len (int): Maximum sequence length.
+
+        Returns:
+            Tuple[int, int]: The range of correction dimensions (low, high), clamped to valid indices.
+        """
+        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):
+        """
+        Computes a linear ramp function used to smooth values between a minimum and maximum range.
+
+        Args:
+            min (float): Minimum value for the ramp function.
+            max (float): Maximum value for the ramp function.
+            dim (int): Dimensionality of the ramp tensor.
+
+        Returns:
+            torch.Tensor: A tensor of shape (dim,) with values linearly interpolated between 0 and 1,
+                clamped to the range [0, 1].
+        """
+        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 seqlen > args.original_seq_len:
+        low, high = find_correction_range(beta_fast, beta_slow, dim, base, args.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) -> torch.Tensor:
+    """
+    Applies rotary positional embeddings to the input tensor.
+
+    Args:
+        x (torch.Tensor): Input tensor with positional embeddings to be applied.
+        freqs_cis (torch.Tensor): Precomputed complex exponential values for positional embeddings.
+
+    Returns:
+        torch.Tensor: Tensor with rotary embeddings applied.
+    """
+    dtype = x.dtype
+    x = torch.view_as_complex(x.float().view(*x.shape[:-1], -1, 2))
+    freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
+    y = torch.view_as_real(x * freqs_cis).flatten(3)
+    return y.to(dtype)
+
+
+def rotate_activation(x: torch.Tensor) -> torch.Tensor:
+    assert x.dtype == torch.bfloat16
+    from fast_hadamard_transform import hadamard_transform
+    hidden_size = x.size(-1)
+    return hadamard_transform(x, scale=hidden_size**-0.5)
+
+
+class Indexer(torch.nn.Module):
+
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.dim: int = args.dim
+        self.n_heads: int = args.index_n_heads
+        self.n_local_heads = args.index_n_heads // world_size
+        self.head_dim: int = args.index_head_dim
+        self.rope_head_dim: int = args.qk_rope_head_dim
+        self.index_topk: int = args.index_topk
+        self.q_lora_rank: int = args.q_lora_rank
+        self.wq_b = Linear(self.q_lora_rank, self.n_heads * self.head_dim)
+        self.wk = Linear(self.dim, self.head_dim)
+        self.k_norm = LayerNorm(self.head_dim)
+        self.weights_proj = Linear(self.dim, self.n_heads, dtype=torch.get_default_dtype())
+        self.softmax_scale = self.head_dim**-0.5
+        self.scale_fmt = args.scale_fmt
+
+        self.register_buffer(
+            "k_cache",
+            torch.zeros(
+                args.max_batch_size, args.max_seq_len, self.head_dim, dtype=torch.float8_e4m3fn),
+            persistent=False)
+        self.register_buffer(
+            "k_scale_cache",
+            torch.zeros(
+                args.max_batch_size,
+                args.max_seq_len,
+                self.head_dim // block_size,
+                dtype=torch.float32),
+            persistent=False)
+
+    def forward(self, x: torch.Tensor, qr: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor,
+                mask: Optional[torch.Tensor]):
+        bsz, seqlen, _ = x.size()
+        end_pos = start_pos + seqlen
+        q = self.wq_b(qr)
+        q = rearrange(q, 'b s (h d) -> b s h d', d=self.head_dim)
+        q_pe, q_nope = torch.split(
+            q, [self.rope_head_dim, self.head_dim - self.rope_head_dim], dim=-1)
+        q_pe = apply_rotary_emb(q_pe, freqs_cis)
+        q = torch.cat([q_pe, q_nope], dim=-1)
+        k = self.wk(x)
+        k = self.k_norm(k)
+        k_pe, k_nope = torch.split(
+            k, [self.rope_head_dim, self.head_dim - self.rope_head_dim], dim=-1)
+        k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis).squeeze(2)
+        k = torch.cat([k_pe, k_nope], dim=-1)
+        q = rotate_activation(q)
+        k = rotate_activation(k)
+        q_fp8, q_scale = act_quant(q, block_size, self.scale_fmt)
+        k_fp8, k_scale = act_quant(k, block_size, self.scale_fmt)
+        self.k_cache[:bsz, start_pos:end_pos] = k_fp8
+        self.k_scale_cache[:bsz, start_pos:end_pos] = k_scale
+        weights = self.weights_proj(x) * self.n_heads**-0.5
+        weights = weights.unsqueeze(-1) * q_scale * self.softmax_scale
+        index_score = fp8_index(q_fp8.contiguous(), weights,
+                                self.k_cache[:bsz, :end_pos].contiguous(),
+                                self.k_scale_cache[:bsz, :end_pos].contiguous())
+        if mask is not None:
+            index_score += mask
+        topk_indices = index_score.topk(min(self.index_topk, end_pos), dim=-1)[1]
+        topk_indices_ = topk_indices.clone()
+        dist.broadcast(topk_indices_, src=0)
+        assert torch.all(topk_indices == topk_indices_), f"{topk_indices=} {topk_indices_=}"
+        return topk_indices
+
+
+def weight_dequant(weight, scale):
+    shape = weight.shape
+    assert weight.dim() == 2
+    weight = weight.view(shape[0] // block_size, block_size, shape[1] // block_size,
+                         block_size).transpose(1, 2).contiguous().view(-1, block_size * block_size)
+    weight = (weight.float() * scale.view(-1, 1).float()).to(torch.get_default_dtype()).view(
+        shape[0] // block_size, shape[1] // block_size, block_size,
+        block_size).transpose(1, 2).contiguous().view(shape)
+    return weight
+
+
+class MLA(nn.Module):
+    """
+    Multi-Head Latent Attention (MLA) Layer.
+
+    Attributes:
+        dim (int): Dimensionality of the input features.
+        n_heads (int): Number of attention heads.
+        n_local_heads (int): Number of local attention heads for distributed systems.
+        q_lora_rank (int): Rank for low-rank query projection.
+        kv_lora_rank (int): Rank for low-rank key/value projection.
+        qk_nope_head_dim (int): Dimensionality of non-positional query/key projections.
+        qk_rope_head_dim (int): Dimensionality of rotary-positional query/key projections.
+        qk_head_dim (int): Total dimensionality of query/key projections.
+        v_head_dim (int): Dimensionality of value projections.
+        softmax_scale (float): Scaling factor for softmax in attention computation.
+    """
+
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        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.kv_lora_rank = args.kv_lora_rank
+        self.qk_nope_head_dim = args.qk_nope_head_dim
+        self.qk_rope_head_dim = args.qk_rope_head_dim
+        self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
+        self.v_head_dim = args.v_head_dim
+
+        self.wq_a = Linear(self.dim, self.q_lora_rank)
+        self.q_norm = RMSNorm(self.q_lora_rank)
+        self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
+        self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
+        self.kv_norm = RMSNorm(self.kv_lora_rank)
+        self.wkv_b = ColumnParallelLinear(self.kv_lora_rank,
+                                          self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
+        self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
+        self.softmax_scale = self.qk_head_dim**-0.5
+        if args.max_seq_len > args.original_seq_len:
+            mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
+            self.softmax_scale = self.softmax_scale * mscale * mscale
+
+        self.indexer = Indexer(args)
+
+        self.register_buffer(
+            "kv_cache",
+            torch.zeros(args.max_batch_size, args.max_seq_len, self.kv_lora_rank),
+            persistent=False)
+        self.register_buffer(
+            "pe_cache",
+            torch.zeros(args.max_batch_size, args.max_seq_len, self.qk_rope_head_dim),
+            persistent=False)
+        self.dequant_wkv_b = None
+
+    def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor,
+                mask: Optional[torch.Tensor]):
+        """
+        Forward pass for the Multi-Head Latent Attention (MLA) Layer.
+
+        Args:
+            x (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim).
+            start_pos (int): Starting position in the sequence for caching.
+            freqs_cis (torch.Tensor): Precomputed complex exponential values for rotary embeddings.
+            mask (Optional[torch.Tensor]): Mask tensor to exclude certain positions from attention.
+
+        Returns:
+            torch.Tensor: Output tensor with the same shape as the input.
+        """
+        bsz, seqlen, _ = x.size()
+        end_pos = start_pos + seqlen
+        qr = self.q_norm(self.wq_a(x))
+        q = self.wq_b(qr)
+        q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
+        q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
+        q_pe = apply_rotary_emb(q_pe, freqs_cis)
+        kv = self.wkv_a(x)
+        kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
+        kv = self.kv_norm(kv)
+        k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis)
+        self.kv_cache[:bsz, start_pos:end_pos] = kv
+        self.pe_cache[:bsz, start_pos:end_pos] = k_pe.squeeze(2)
+        if mask is not None:  # MHA prefill
+            q = torch.cat([q_nope, q_pe], dim=-1)
+            kv = self.wkv_b(kv)
+            kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
+            k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
+            k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
+            scores = torch.einsum("bshd,bthd->bsht", q.float(), k.float()) * self.softmax_scale
+
+            # indexer
+            topk_indices = self.indexer(x, qr, start_pos, freqs_cis, mask)
+            index_mask = torch.full((bsz, seqlen, seqlen), float("-inf"),
+                                    device=x.device).scatter_(-1, topk_indices, 0)
+            index_mask += mask
+            scores += index_mask.unsqueeze(2)
+
+            scores = scores.softmax(dim=-1, dtype=torch.float32)
+            x = torch.einsum("bsht,bthd->bshd", scores.type_as(x), v)
+        else:  # MHA decode
+            if self.dequant_wkv_b is None and self.wkv_b.scale is not None:
+                self.dequant_wkv_b = weight_dequant(self.wkv_b.weight, self.wkv_b.scale)
+            wkv_b = self.wkv_b.weight if self.dequant_wkv_b is None else self.dequant_wkv_b
+            wkv_b = wkv_b.view(self.n_local_heads, -1, self.kv_lora_rank)
+            q_nope = torch.einsum("bshd,hdc->bshc", q_nope, wkv_b[:, :self.qk_nope_head_dim])
+            scores = (torch.einsum("bshc,btc->bsht", q_nope.float(),
+                                   self.kv_cache[:bsz, :end_pos].float()) +
+                      torch.einsum("bshr,btr->bsht", q_pe.float(),
+                                   self.pe_cache[:bsz, :end_pos].float())) * self.softmax_scale
+
+            # indexer
+            topk_indices = self.indexer(x, qr, start_pos, freqs_cis, mask)
+            index_mask = torch.full((bsz, 1, end_pos), float("-inf"),
+                                    device=x.device).scatter_(-1, topk_indices, 0)
+            scores += index_mask.unsqueeze(2)
+
+            scores = scores.softmax(dim=-1, dtype=torch.float32)
+            x = torch.einsum("bsht,btc->bshc", scores.type_as(x), self.kv_cache[:bsz, :end_pos])
+            x = torch.einsum("bshc,hdc->bshd", x, wkv_b[:, -self.v_head_dim:])
+        x = self.wo(x.flatten(2))
+        return x
+
+
+class MLP(nn.Module):
+    """
+    Multi-Layer Perceptron (MLP) used as a feed-forward layer.
+
+    Attributes:
+        w1 (nn.Module): Linear layer for input-to-hidden transformation.
+        w2 (nn.Module): Linear layer for hidden-to-output transformation.
+        w3 (nn.Module): Additional linear layer for feature transformation.
+    """
+
+    def __init__(self, dim: int, inter_dim: int, reduce_output: bool = True):
+        """
+        Initializes the MLP layer.
+
+        Args:
+            dim (int): Input and output dimensionality.
+            inter_dim (int): Hidden layer dimensionality.
+        """
+        super().__init__()
+        self.w1 = ColumnParallelLinear(dim, inter_dim)
+        self.w2 = RowParallelLinear(inter_dim, dim, reduce_output=reduce_output)
+        self.w3 = ColumnParallelLinear(dim, inter_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the MLP layer.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Output tensor after MLP computation.
+        """
+        return self.w2((F.silu(self.w1(x).float()) * self.w3(x).float()).type_as(x))
+
+
+class Gate(nn.Module):
+    """
+    Gating mechanism for routing inputs in a mixture-of-experts (MoE) model.
+
+    Attributes:
+        dim (int): Dimensionality of input features.
+        topk (int): Number of top experts activated for each input.
+        n_groups (int): Number of groups for routing.
+        topk_groups (int): Number of groups to route inputs to.
+        score_func (str): Scoring function ('softmax' or 'sigmoid').
+        route_scale (float): Scaling factor for routing weights.
+        weight (torch.nn.Parameter): Learnable weights for the gate.
+        bias (Optional[torch.nn.Parameter]): Optional bias term for the gate.
+    """
+
+    def __init__(self, args: ModelArgs):
+        """
+        Initializes the Gate module.
+
+        Args:
+            args (ModelArgs): Model arguments containing gating parameters.
+        """
+        super().__init__()
+        self.dim = args.dim
+        self.topk = args.n_activated_experts
+        self.n_groups = args.n_expert_groups
+        self.topk_groups = args.n_limited_groups
+        self.score_func = args.score_func
+        self.route_scale = args.route_scale
+        self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
+        self.bias = nn.Parameter(torch.empty(args.n_routed_experts,
+                                             dtype=torch.float32)) if self.dim == 7168 else None
+
+    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
+        """
+        Forward pass for the gating mechanism.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            Tuple[torch.Tensor, torch.Tensor]: Routing weights and selected expert indices.
+        """
+        scores = linear(x.float(), self.weight.float())
+        if self.score_func == "softmax":
+            scores = scores.softmax(dim=-1)
+        else:
+            scores = scores.sigmoid()
+        original_scores = scores
+        if self.bias is not None:
+            scores = scores + self.bias
+        if self.n_groups > 1:
+            scores = scores.view(x.size(0), self.n_groups, -1)
+            if self.bias is None:
+                group_scores = scores.amax(dim=-1)
+            else:
+                group_scores = scores.topk(2, dim=-1)[0].sum(dim=-1)
+            indices = group_scores.topk(self.topk_groups, dim=-1)[1]
+            mask = scores.new_ones(x.size(0), self.n_groups, dtype=bool).scatter_(1, indices, False)
+            scores = scores.masked_fill_(mask.unsqueeze(-1), float("-inf")).flatten(1)
+        indices = scores.topk(self.topk, dim=-1)[1]
+        weights = original_scores.gather(1, indices)
+        if self.score_func == "sigmoid":
+            weights /= weights.sum(dim=-1, keepdim=True)
+        weights *= self.route_scale
+        return weights, indices
+
+
+class Expert(nn.Module):
+    """
+    Expert layer for Mixture-of-Experts (MoE) models.
+
+    Attributes:
+        w1 (nn.Module): Linear layer for input-to-hidden transformation.
+        w2 (nn.Module): Linear layer for hidden-to-output transformation.
+        w3 (nn.Module): Additional linear layer for feature transformation.
+    """
+
+    def __init__(self, dim: int, inter_dim: int):
+        """
+        Initializes the Expert layer.
+
+        Args:
+            dim (int): Input and output dimensionality.
+            inter_dim (int): Hidden layer dimensionality.
+        """
+        super().__init__()
+        self.w1 = Linear(dim, inter_dim)
+        self.w2 = Linear(inter_dim, dim)
+        self.w3 = Linear(dim, inter_dim)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the Expert layer.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Output tensor after expert computation.
+        """
+        return self.w2((F.silu(self.w1(x).float()) * self.w3(x).float()).type_as(x))
+
+
+class MoE(nn.Module):
+    """
+    Mixture-of-Experts (MoE) module.
+
+    Attributes:
+        dim (int): Dimensionality of input features.
+        n_routed_experts (int): Total number of experts in the model.
+        n_local_experts (int): Number of experts handled locally in distributed systems.
+        n_activated_experts (int): Number of experts activated for each input.
+        gate (nn.Module): Gating mechanism to route inputs to experts.
+        experts (nn.ModuleList): List of expert modules.
+        shared_experts (nn.Module): Shared experts applied to all inputs.
+    """
+
+    def __init__(self, args: ModelArgs):
+        """
+        Initializes the MoE module.
+
+        Args:
+            args (ModelArgs): Model arguments containing MoE parameters.
+        """
+        super().__init__()
+        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(args)
+        self.experts = nn.ModuleList([
+            Expert(args.dim, args.moe_inter_dim)
+            if self.experts_start_idx <= i < self.experts_end_idx else None
+            for i in range(self.n_routed_experts)
+        ])
+        self.shared_experts = MLP(
+            args.dim, args.n_shared_experts * args.moe_inter_dim, reduce_output=False)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        """
+        Forward pass for the MoE module.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            torch.Tensor: Output tensor after expert routing and computation.
+        """
+        shape = x.size()
+        x = x.view(-1, self.dim)
+        weights, indices = self.gate(x)
+        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]
+        y += self.shared_experts(x)
+        if world_size > 1:
+            dist.all_reduce(y)
+        return y.type_as(x).view(shape)
+
+
+class Block(nn.Module):
+    """
+    Transformer block combining attention and feed-forward layers.
+
+    Attributes:
+        attn (nn.Module): Attention layer (MLA).
+        ffn (nn.Module): Feed-forward network (MLP or MoE).
+        attn_norm (nn.Module): Layer normalization for attention.
+        ffn_norm (nn.Module): Layer normalization for feed-forward network.
+    """
+
+    def __init__(self, layer_id: int, args: ModelArgs):
+        """
+        Initializes the Transformer block.
+
+        Args:
+            layer_id (int): Layer index in the transformer.
+            args (ModelArgs): Model arguments containing block parameters.
+        """
+        super().__init__()
+        self.attn = MLA(args)
+        self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
+        self.attn_norm = RMSNorm(args.dim)
+        self.ffn_norm = RMSNorm(args.dim)
+
+    def forward(self, x: torch.Tensor, residual: torch.Tensor, start_pos: int,
+                freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
+        """
+        Forward pass for the Transformer block.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+            start_pos (int): Starting position in the sequence.
+            freqs_cis (torch.Tensor): Precomputed complex exponential values for rotary embeddings.
+            mask (Optional[torch.Tensor]): Mask tensor to exclude certain positions from attention.
+
+        Returns:
+            torch.Tensor: Output tensor after block computation.
+        """
+        if residual is None:
+            x, residual = self.attn_norm(x), x
+        else:
+            x, residual = self.attn_norm(x, residual)
+        x = self.attn(x, start_pos, freqs_cis, mask)
+        x, residual = self.ffn_norm(x, residual)
+        x = self.ffn(x)
+        return x, residual
+
+
+class Transformer(nn.Module):
+    """
+    Transformer model with positional embeddings, multiple layers, and output projection.
+
+    Attributes:
+        max_seq_len (int): Maximum sequence length for the transformer.
+        embed (nn.Module): Embedding layer for input tokens.
+        layers (torch.nn.ModuleList): List of transformer blocks.
+        norm (nn.Module): Layer normalization applied after all blocks.
+        head (nn.Module): Output projection layer mapping to vocabulary size.
+        freqs_cis (torch.Tensor): Precomputed complex exponential values for rotary embeddings.
+    """
+
+    def __init__(self, args: ModelArgs):
+        """
+        Initializes the Transformer model.
+
+        Args:
+            args (ModelArgs): Model arguments containing transformer parameters.
+        """
+        global world_size, rank
+        world_size = dist.get_world_size() if dist.is_initialized() else 1
+        rank = dist.get_rank() if dist.is_initialized() else 0
+        Linear.dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
+        Linear.scale_fmt = args.scale_fmt
+        super().__init__()
+        self.max_seq_len = args.max_seq_len
+        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)
+        # lm_head in the checkpoint is stored in bf16, while the parameter here is stored in fp32 for easier computation of logits later.
+        self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.float32)
+        self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)
+
+    @torch.inference_mode()
+    def forward(self, tokens: torch.Tensor, start_pos: int = 0):
+        """
+        Forward pass for the Transformer model.
+
+        Args:
+            tokens (torch.Tensor): Input tensor of token IDs with shape (batch_size, seq_len).
+            start_pos (int, optional): Starting position in the sequence for rotary embeddings. Defaults to 0.
+
+        Returns:
+            torch.Tensor: Logits tensor of shape (batch_size, vocab_size).
+        """
+        seqlen = tokens.size(1)
+        freqs_cis = self.freqs_cis[start_pos:start_pos + seqlen]
+        mask = torch.full(
+            (seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1) if seqlen > 1 else None
+        h, residual = self.embed(tokens), None
+        for layer in self.layers:
+            h, residual = layer(h, residual, start_pos, freqs_cis, mask)
+        h, _ = self.norm(h, residual)
+        logits = self.head(h[:, -1].float())
+        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
+
+
+if __name__ == "__main__":
+    torch.set_default_dtype(torch.bfloat16)
+    torch.set_default_device("cuda")
+    torch.manual_seed(0)
+    args = ModelArgs()
+    x = torch.randint(0, args.vocab_size, (2, 128))
+    model = Transformer(args)
+    print(model(x).size())

examples/deepseek_v32/inference/requirements.txt

+torch
+transformers
+safetensors
+fast_hadamard_transform
+tilelang==0.1.6
\ No newline at end of file

examples/deepseek_v32/sparse_mla_fwd.py

@@ -231,19 +231,15 @@ def ref_sparse_mla_fwd_interface(q, kv, indices, sm_scale=None, is_casual=True):
     return o.to(torch.bfloat16)
 
 
-def test_sparse_mla_fwd():
-    B, S, SKV, H, HKV, DQK, DV, topk, dtype = (
-        1,
-        4096,
-        32768,
-        128,
-        1,
-        576,
-        512,
-        2048,
-        torch.bfloat16,
-    )
-
+def test_sparse_mla_fwd(B=1,
+                        S=4096,
+                        SKV=4096,
+                        H=128,
+                        HKV=1,
+                        DQK=576,
+                        DV=512,
+                        topk=2048,
+                        dtype=torch.bfloat16):
     torch.random.manual_seed(0)
     q = torch.randn((B, S, H, DQK), dtype=dtype, device="cuda").requires_grad_(True)
     kv = torch.randn((B, SKV, HKV, DQK), dtype=dtype, device="cuda").requires_grad_(True)
@@ -273,4 +269,5 @@ def test_sparse_mla_fwd():
 
 
 if __name__ == "__main__":
-    test_sparse_mla_fwd()
+    test_sparse_mla_fwd(
+        B=1, S=4096, SKV=32768, H=128, HKV=1, DQK=576, DV=512, topk=2048, dtype=torch.bfloat16)

examples/deepseek_v32/sparse_mla_fwd_pipelined.py

@@ -397,14 +397,17 @@ def ref_sparse_mla_fwd_interface(q,
     return o.to(torch.bfloat16)
 
 
-def test_sparse_mla_fwd(test_correctness=False):
+def test_sparse_mla_fwd_pipelined(B=1,
+                                  S=4096,
+                                  SKV=4096,
+                                  H=128,
+                                  HKV=1,
+                                  DQK=576,
+                                  DV=512,
+                                  topk=2048,
+                                  dtype=torch.bfloat16,
+                                  q_start_s_index=1024):
     KV_stride = 1
-    if test_correctness:
-        B, S, SKV, H, HKV, DQK, DV, topk, dtype = 1, 1024, 2048, 128, 1, 576, 512, 2048, torch.bfloat16
-        q_start_s_index = 1024
-    else:
-        B, S, SKV, H, HKV, DQK, DV, topk, dtype = 1, 4096, 8192, 128, 1, 576, 512, 2048, torch.bfloat16
-        q_start_s_index = 4096 * 64
 
     torch.random.manual_seed(0)
     q = torch.randn((B, S, H, DQK), dtype=dtype, device='cuda').requires_grad_(True) / 10
@@ -426,14 +429,14 @@ def test_sparse_mla_fwd(test_correctness=False):
 
     def fn():
         out, lse = kernel(q, kv, indices, q_start_s_index_t)
-        if q_start_s_index == 0 and kv_stride > 1:
-            out[:, :kv_stride - 1, :, :] = 0
+        if q_start_s_index == 0 and KV_stride > 1:
+            out[:, :KV_stride - 1, :, :] = 0
         return out, lse
 
     tl_out, tl_lse = fn()
     ref_out = ref_sparse_mla_fwd_interface(q, kv, indices, q_start_s_index, KV_stride)
-    print(f"tl_out: {tl_out}")
-    print(f"ref_out: {ref_out}")
+    # print(f"tl_out: {tl_out}")
+    # print(f"ref_out: {ref_out}")
 
     torch.testing.assert_close(tl_out, ref_out, rtol=1e-3, atol=1e-3)
 
@@ -452,4 +455,9 @@ if __name__ == "__main__":
     parser = argparse.ArgumentParser()
     parser.add_argument("--test_correctness", action="store_true")
     args = parser.parse_args()
-    test_sparse_mla_fwd(args.test_correctness)
+    if args.test_correctness:
+        B, S, SKV, H, HKV, DQK, DV, topk, dtype = 1, 1024, 2048, 128, 1, 576, 512, 2048, torch.bfloat16
+    else:
+        B, S, SKV, H, HKV, DQK, DV, topk, dtype = 1, 4096, 8192, 128, 1, 576, 512, 2048, torch.bfloat16
+    test_sparse_mla_fwd(B, S, SKV, H, HKV, DQK, DV, topk, dtype)
+    test_sparse_mla_fwd(B, S, SKV, H, HKV, DQK, DV, topk, dtype)

examples/deepseek_v32/test_tilelang_example_deepseek_v32.py

+# ruff: noqa
+import tilelang.testing
+
+from topk_selector import test_topk_selector
+from fp8_lighting_indexer import test_fp8_lighting_indexer
+from sparse_mla_fwd import test_sparse_mla_fwd
+from sparse_mla_fwd_pipelined import test_sparse_mla_fwd_pipelined
+
+
+def test_example_topk_selector():
+    test_topk_selector()
+
+
+def test_example_fp8_lighting_indexer():
+    test_fp8_lighting_indexer()
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
+def test_example_sparse_mla_fwd():
+    # small shapes for testing
+    test_sparse_mla_fwd(S=1024, SKV=2048, H=128, HKV=1, DQK=576, DV=512, topk=256)
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
+def test_example_sparse_mla_fwd_pipelined():
+    # small shapes for testing
+    test_sparse_mla_fwd_pipelined(S=1024, SKV=2048, H=128, HKV=1, DQK=576, DV=512, topk=256)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

examples/deepseek_v32/topk_selector.py

+import torch
+import tilelang
+import tilelang.language as T
+
+pass_configs = {
+    tilelang.PassConfigKey.TL_DISABLE_THREAD_STORAGE_SYNC: True,
+}
+
+
+def convert_to_uint16(x):
+    hval = T.Cast("float16", x)
+    bits_uint = T.reinterpret("uint16", hval)
+    bits_uint = T.if_then_else(x < 0, ~bits_uint & (0xFFFF), bits_uint | (0x8000))
+    return bits_uint >> 8
+
+
+def convert_to_uint32(x):
+    bits_uint = T.reinterpret("uint32", x)
+    bits_uint = T.if_then_else(
+        x < 0,
+        ~bits_uint & T.Cast("uint32", (0xFFFFFFFF)),
+        bits_uint | T.Cast("uint32", (0x80000000)),
+    )
+    return bits_uint
+
+
+@tilelang.jit(pass_configs=pass_configs)
+def tl_topk_impl(topk, in_dtype="float32", out_dtype="int32"):
+    batch = T.symbolic("batch")
+    seq_len = T.symbolic("seq_len")
+    RADIX = 1 << 8
+    BLOCK_SIZE = 1024
+    SMEM_INPUT_SIZE = 4096  # assume the threshold bucket size after first pass is less than 4K
+
+    @T.prim_func
+    def tl_topk_kernel(
+        input: T.Tensor[(batch, seq_len), in_dtype],
+        index: T.Tensor[(batch, topk), out_dtype],
+        starts: T.Tensor[(batch), out_dtype],
+        ends: T.Tensor[(batch), out_dtype],
+    ):
+        with T.Kernel(batch, threads=BLOCK_SIZE) as (bx):
+            tx = T.get_thread_binding()
+
+            s_threshold_bin_id = T.alloc_shared([1], "int32")
+            s_histogram = T.alloc_shared([RADIX + 1], "int32")
+            s_num_input = T.alloc_shared([2], "int32")
+            s_input_idx = T.alloc_shared([2, SMEM_INPUT_SIZE], "int32")
+
+            l_threshold_bin_id = T.alloc_var("int32")
+            l_new_topk = T.alloc_var("int32")
+            l_num_input = T.alloc_var("int32")
+            l_bin_id32 = T.alloc_var("int32")
+            l_val = T.alloc_var("int32")
+            l_start_pos = T.alloc_var("int32")
+            l_start_idx = T.alloc_var("int32")
+            l_end_idx = T.alloc_var("int32")
+            l_out_pos = T.alloc_var("int32")
+
+            l_new_topk = topk
+            l_start_idx = starts[bx]
+            l_end_idx = ends[bx]
+
+            # stage 1: use 8bit to do quick topk
+            T.fill(s_histogram, 0)
+            T.fill(s_num_input[0], 0)
+
+            T.sync_threads()
+            for s in T.serial(T.ceildiv(seq_len, BLOCK_SIZE)):
+                input_idx = s * BLOCK_SIZE + tx
+                if input_idx < l_end_idx and input_idx >= l_start_idx and input_idx < seq_len:
+                    inval_int16 = convert_to_uint16(input[bx, input_idx])
+                    T.atomic_add(s_histogram[inval_int16], 1)
+            T.sync_threads()
+
+            # cumsum
+            if tx < RADIX:
+                for i in T.serial(8):
+                    offset = 1 << i
+                    T.sync_threads(3, RADIX)
+                    if tx < RADIX - offset:
+                        l_val = s_histogram[tx] + s_histogram[tx + offset]
+                    T.sync_threads(3, RADIX)
+                    if tx < RADIX - offset:
+                        s_histogram[tx] = l_val
+
+                # find threshold bin id
+                T.sync_threads(3, RADIX)
+                if s_histogram[tx] > l_new_topk and s_histogram[tx + 1] <= l_new_topk:
+                    s_threshold_bin_id[0] = tx
+            T.sync_threads()
+            l_threshold_bin_id = s_threshold_bin_id[0]
+            l_new_topk = l_new_topk - s_histogram[l_threshold_bin_id + 1]
+            T.sync_threads()
+
+            # collect all elements with exponent ≥ threshold
+            for s in T.serial(T.ceildiv(seq_len, BLOCK_SIZE)):
+                T.sync_threads()
+                input_idx = s * BLOCK_SIZE + tx
+                if input_idx < l_end_idx and input_idx >= l_start_idx and input_idx < seq_len:
+                    bin_id = convert_to_uint16(input[bx, input_idx])
+                    l_bin_id32 = T.Cast("int32", bin_id)
+                    if l_bin_id32 > l_threshold_bin_id:
+                        # need a pos = T.atomic_add(s_histogram[bin_id32+1], 1)
+                        pos = T.atomic_add(s_histogram[l_bin_id32 + 1], 1, return_prev=True)
+                        index[bx, pos] = input_idx
+
+                    elif l_bin_id32 == l_threshold_bin_id and l_new_topk > 0:
+                        # pos = s_num_input[0]
+                        pos = T.atomic_add(s_num_input[0], 1, return_prev=True)
+                        s_input_idx[0, pos] = input_idx
+
+            # stage 2: tail pass
+            for round in T.serial(4):
+                if l_new_topk <= 0:
+                    T.loop_break()
+
+                r_idx = round % 2
+                l_start_pos = topk - l_new_topk
+
+                T.sync_threads()
+                T.fill(s_histogram, 0)
+                if tx == 0:
+                    s_num_input[r_idx ^ 1] = 0
+                T.sync_threads()
+
+                l_num_input = s_num_input[r_idx]
+                for s in T.serial(T.ceildiv(l_num_input, BLOCK_SIZE)):
+                    if s * BLOCK_SIZE + tx < l_num_input:
+                        l_bin_id32 = T.Cast("int32", ((
+                            convert_to_uint32(input[bx, s_input_idx[r_idx, s * BLOCK_SIZE + tx]]) >>
+                            (24 - round * 8)) & 0xFF))
+                        T.atomic_add(s_histogram[l_bin_id32], 1)
+                T.sync_threads()
+                # cumsum
+                if tx < RADIX:
+                    for i in T.serial(8):
+                        offset = 1 << i
+                        T.sync_threads(3, RADIX)
+                        if tx < RADIX - offset:
+                            l_val = s_histogram[tx] + s_histogram[tx + offset]
+                        T.sync_threads(3, RADIX)
+                        if tx < RADIX - offset:
+                            s_histogram[tx] = l_val
+
+                    # find threshold bin id
+                    T.sync_threads(3, RADIX)
+                    if s_histogram[tx] > l_new_topk and s_histogram[tx + 1] <= l_new_topk:
+                        s_threshold_bin_id[0] = tx
+                T.sync_threads()
+
+                l_threshold_bin_id = s_threshold_bin_id[0]
+                l_new_topk = l_new_topk - s_histogram[l_threshold_bin_id + 1]
+                T.sync_threads()
+
+                for s in T.serial(T.ceildiv(l_num_input, BLOCK_SIZE)):
+                    T.sync_threads()
+                    if s * BLOCK_SIZE + tx < l_num_input:
+                        l_bin_id32 = T.Cast("int32", ((
+                            convert_to_uint32(input[bx, s_input_idx[r_idx, s * BLOCK_SIZE + tx]]) >>
+                            (24 - round * 8)) & 0xFF))
+                        if l_bin_id32 > l_threshold_bin_id:
+                            pos = T.atomic_add(
+                                s_histogram[l_bin_id32 + 1], 1, return_prev=True) + l_start_pos
+                            index[bx, pos] = s_input_idx[r_idx, s * BLOCK_SIZE + tx]
+                        elif l_bin_id32 == l_threshold_bin_id and l_new_topk > 0:
+                            if round == 3:
+                                l_out_pos = T.atomic_add(
+                                    s_histogram[l_bin_id32 + 1], 1, return_prev=True) + l_start_pos
+                                if l_out_pos < topk:
+                                    index[bx, l_out_pos] = s_input_idx[r_idx, s * BLOCK_SIZE + tx]
+                            else:
+                                pos = T.atomic_add(s_num_input[r_idx ^ 1], 1, return_prev=True)
+                                s_input_idx[r_idx ^ 1, pos] = s_input_idx[r_idx,
+                                                                          s * BLOCK_SIZE + tx]
+
+    return tl_topk_kernel
+
+
+def tl_topk(input, starts, ends, topk):
+    batch, seq_len = input.shape
+    indexes = torch.zeros(batch, topk, dtype=torch.int32, device=input.device)
+    kernel = tl_topk_impl(topk)
+    kernel(input, indexes, starts, ends)
+    return indexes
+
+
+def test_topk_selector(batch=64, seq_len=32 * 1024, topk=2048):
+
+    batch = 64
+    seq_len = 32 * 1024
+    topk = 2048
+    torch.manual_seed(1)
+    input = torch.randn(batch, seq_len, dtype=torch.float32).cuda()
+    starts = torch.zeros(batch, dtype=torch.int32).cuda()
+    ends = torch.ones(batch, dtype=torch.int32).cuda() * seq_len
+
+    indexes = tl_topk(input, starts, ends, topk)
+    print(indexes)
+
+    indexes_ref = torch.topk(input, topk, dim=-1)[1]
+    print(indexes_ref)
+
+    # indexes_ref = fast_topk(input, topk)
+    # print(indexes_ref)
+
+    # Calculate intersection of out_ref and out_trt
+    for i in range(batch):
+        ref_np = indexes_ref[i].cpu().to(torch.int32).numpy()
+        trt_np = indexes[i].cpu().to(torch.int32).numpy()
+
+        set_ref = set(ref_np)
+        set_trt = set(trt_np)
+        intersection = set_ref & set_trt
+        print("selected/all:", len(intersection), "/", len(set_ref), "=",
+              len(intersection) / len(set_ref))
+
+    # Performance test with CUDA events
+
+    torch.cuda.synchronize()
+    start_event = torch.cuda.Event(enable_timing=True)
+    end_event = torch.cuda.Event(enable_timing=True)
+
+    # Warmup
+    for _ in range(5):
+        _ = tl_topk(input, starts, ends, topk)
+    torch.cuda.synchronize()
+
+    n_iters = 20
+    start_event.record()
+    for _ in range(n_iters):
+        _ = tl_topk(input, starts, ends, topk)
+    end_event.record()
+    torch.cuda.synchronize()
+    elapsed_time_ms = start_event.elapsed_time(end_event)
+    print(f"Average tl_topk time: {elapsed_time_ms / n_iters:.3f} ms")
+
+    # Torch topk time
+    start_event.record()
+    for _ in range(n_iters):
+        _ = torch.topk(input, topk, dim=-1)[1]
+    end_event.record()
+    torch.cuda.synchronize()
+    elapsed_time_ms = start_event.elapsed_time(end_event)
+    print(f"Average torch.topk time: {elapsed_time_ms / n_iters:.3f} ms")
+
+
+if __name__ == "__main__":
+    test_topk_selector()

pyproject.toml

@@ -57,3 +57,4 @@ ignore = [
 ]
 [tool.ruff.lint.per-file-ignores]
 "3rdparty/**/*" = ["ALL"]
+"examples/deepseek_v32/inference/**/*" = ["ALL"]
\ No newline at end of file