init

examples/attention_sink/example_mha_sink_fwd_bhsd.py

+# Modified from tilelang/examples/flash_attention/example_mha_fwd_bhsd.py
+
+import torch
+import tilelang
+from tilelang.autotuner import autotune
+from tilelang.profiler import do_bench
+import tilelang.language as T
+from tilelang.layout import make_swizzled_layout
+import itertools
+import argparse
+from typing import Optional
+
+
+def get_configs():
+    iter_params = dict(block_M=[128], block_N=[128], num_stages=[0, 1, 2], threads=[128, 256])
+    return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())]
+
+
+@autotune(configs=get_configs(), warmup=500, rep=100)
+@tilelang.jit(
+    out_idx=[3], pass_configs={
+        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+    })
+def flashattn(
+        batch,
+        heads,
+        seq_q,
+        seq_kv,
+        dim,
+        window_size=None,  # None for full attention
+        sm_scale=None,
+        block_M=64,
+        block_N=64,
+        num_stages=1,
+        threads=128,
+        dtype: str = "float16"):
+    if window_size is not None:
+        assert window_size % block_N == 0, "window_size must be divisible by block_N"
+
+    if sm_scale is None:
+        sm_scale = (1.0 / dim)**0.5
+    scale = sm_scale * 1.44269504  # log2(e)
+    q_shape = [batch, heads, seq_q, dim]
+    kv_shape = [batch, heads, seq_kv, dim]
+    accum_dtype = "float"
+
+    past_len = seq_kv - seq_q
+    assert past_len >= 0, "seq_kv must be greater than or equal to seq_q"
+
+    @T.macro
+    def MMA0(
+        K: T.Tensor(kv_shape, dtype),
+        Q_shared: T.SharedBuffer([block_M, dim], dtype),
+        K_shared: T.SharedBuffer([block_N, dim], dtype),
+        acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
+        k: T.int32,
+        bx: T.int32,
+        by: T.int32,
+        bz: T.int32,
+    ):
+        T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared)
+        for i, j in T.Parallel(block_M, block_N):
+            q_idx = bx * block_M + i + past_len
+            k_idx = k * block_N + j
+            if window_size is not None:
+                acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0,
+                                             -T.infinity(acc_s.dtype))
+            else:
+                acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype))
+        T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+
+    @T.macro
+    def MMA1(
+        V: T.Tensor(kv_shape, dtype),
+        V_shared: T.SharedBuffer([block_M, dim], dtype),
+        acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
+        acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+        k: T.int32,
+        by: T.int32,
+        bz: T.int32,
+    ):
+        T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared)
+        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+
+    @T.macro
+    def Softmax(
+            acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
+            acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
+            scores_max: T.FragmentBuffer([block_M], accum_dtype),
+            scores_max_prev: T.FragmentBuffer([block_M], accum_dtype),
+            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
+            scores_sum: T.FragmentBuffer([block_M], accum_dtype),
+            logsum: T.FragmentBuffer([block_M], accum_dtype),
+    ):
+        T.copy(scores_max, scores_max_prev)
+        T.fill(scores_max, -T.infinity(accum_dtype))
+        T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+        # To do causal softmax, we need to set the scores_max to 0 if it is -inf
+        # This process is called Check_inf in FlashAttention3 code, and it only need to be done
+        # NOTE(wt): check_inf is necessary for sliding window attention.
+        for i in T.Parallel(block_M):
+            if window_size is not None:
+                scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0,
+                                               scores_max[i])
+            scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+
+        for i, j in T.Parallel(block_M, block_N):
+            # Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
+            # max * log_2(e)) This allows the compiler to use the ffma
+            # instruction instead of fadd and fmul separately.
+            acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+        T.reduce_sum(acc_s, scores_sum, dim=1)
+        for i in T.Parallel(block_M):
+            logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+        T.copy(acc_s, acc_s_cast)
+
+    @T.macro
+    def Rescale(
+            acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
+    ):
+        for i, j in T.Parallel(block_M, dim):
+            acc_o[i, j] *= scores_scale[i]
+
+    @T.prim_func
+    def main(
+            Q: T.Tensor(q_shape, dtype),
+            K: T.Tensor(kv_shape, dtype),
+            V: T.Tensor(kv_shape, dtype),
+            Output: T.Tensor(q_shape, dtype),
+            Sinks: T.Tensor([heads], dtype),
+    ):
+        with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz):
+            Q_shared = T.alloc_shared([block_M, dim], dtype)
+            K_shared = T.alloc_shared([block_N, dim], dtype)
+            V_shared = T.alloc_shared([block_N, dim], dtype)
+            O_shared = T.alloc_shared([block_M, dim], dtype)
+            acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
+            acc_s_cast = T.alloc_fragment([block_M, block_N], dtype)
+            acc_o = T.alloc_fragment([block_M, dim], accum_dtype)
+            scores_max = T.alloc_fragment([block_M], accum_dtype)
+            scores_max_prev = T.alloc_fragment([block_M], accum_dtype)
+            scores_scale = T.alloc_fragment([block_M], accum_dtype)
+            scores_sum = T.alloc_fragment([block_M], accum_dtype)
+            logsum = T.alloc_fragment([block_M], accum_dtype)
+            sinks = T.alloc_fragment([block_M], dtype)
+
+            T.annotate_layout({
+                Q_shared: make_swizzled_layout(Q_shared),
+                K_shared: make_swizzled_layout(K_shared),
+                V_shared: make_swizzled_layout(V_shared),
+                O_shared: make_swizzled_layout(O_shared),
+            })
+
+            T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared)
+            T.fill(acc_o, 0)
+            T.fill(logsum, 0)
+            T.fill(scores_max, -T.infinity(accum_dtype))
+            for i in T.Parallel(block_M):
+                sinks[i] = Sinks[by]
+
+            end = T.min(
+                T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N))
+
+            start = T.alloc_local([1], 'int32')
+            if window_size is not None:
+                start[0] = T.max(0, (bx * block_M + past_len - window_size) // block_N)
+            else:
+                start[0] = 0
+
+            for k in T.Pipelined(start[0], end, num_stages=num_stages):
+                MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz)
+                Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum,
+                        logsum)
+                Rescale(acc_o, scores_scale)
+                MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz)
+            for i in T.Parallel(block_M):
+                logsum[i] += T.exp2(sinks[i] * 1.44269504 -
+                                    scores_max[i] * scale)  # The only change for attention sink
+            for i, j in T.Parallel(block_M, dim):
+                acc_o[i, j] /= logsum[i]
+            T.copy(acc_o, O_shared)
+            T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :])
+
+    return main
+
+
+# Modified from https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py
+def ref_program(query: torch.Tensor,
+                key: torch.Tensor,
+                value: torch.Tensor,
+                sinks: torch.Tensor,
+                sliding_window: Optional[int] = None,
+                dtype: torch.dtype = torch.float16) -> torch.Tensor:
+
+    query = query.transpose(1, 2).contiguous().unsqueeze(
+        3)  # align with the original function's interface
+    key = key.transpose(1, 2).contiguous()
+    value = value.transpose(1, 2).contiguous()
+
+    batch_size, num_queries, num_key_value_heads, num_key_value_groups, head_dim = query.shape
+    batch_size, num_keys, num_key_value_heads, head_dim = key.shape
+    start_q = num_keys - num_queries
+
+    sm_scale: float = 1.0 / head_dim**0.5
+
+    sinks = sinks.view(1, num_key_value_heads, num_key_value_groups, 1, 1).float()
+    key = key.unsqueeze(3)
+    value = value.unsqueeze(3)
+
+    pos_keys = torch.arange(num_keys, device=query.device)
+    pos_queries = torch.arange(num_queries, device=query.device) + start_q
+    mask = pos_keys[None, :] > pos_queries[:, None]
+    mask = mask.float().masked_fill(mask, float("-inf"))
+
+    if sliding_window:
+        too_old = pos_keys[None, :] < (pos_queries[:, None] - sliding_window + 1)
+        mask.masked_fill_(too_old, float("-inf"))
+
+    logits = torch.einsum("bqhmd,bkhmd->bhmqk", query.float(), key.float()) * sm_scale
+    logits = logits + mask[None, None, None, :, :]
+
+    logits_max = torch.max(logits, dim=-1, keepdim=True).values
+    logits_or_sinks_max = torch.maximum(sinks, logits_max)
+    sinks = torch.exp(sinks - logits_or_sinks_max)
+    unnormalized_scores = torch.exp(logits - logits_or_sinks_max)
+    normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + sinks
+    scores = unnormalized_scores / normalizer
+
+    output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float())
+
+    output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups,
+                            head_dim).to(dtype)
+    return output.transpose(1, 2).contiguous()
+
+
+def gen_inputs(
+        B,
+        H,
+        Sq,
+        Skv,
+        D,
+        dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    query = torch.randn([B, H, Sq, D], dtype=dtype, device='cuda')
+    key = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda')
+    value = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda')
+    sinks = torch.randn([H], dtype=dtype, device='cuda')
+    return query, key, value, sinks
+
+
+def main(batch: int = 1,
+         heads: int = 1,
+         seq_q: int = 256,
+         seq_kv: int = 256,
+         dim: int = 128,
+         window_size: int | None = None,
+         dtype: str = "float16",
+         tune: bool = False):
+    torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype]
+    if window_size is not None:
+        print('Using sliding window attention.')
+        assert window_size <= seq_q
+        flops_per_matmul = 2.0 * batch * heads * min(
+            window_size, seq_kv // 2) * seq_q * dim  # just a rough estimation
+    else:
+        print('Using full attention.')
+        flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5
+    total_flops = 2 * flops_per_matmul
+
+    if tune:
+        kernel = flashattn(batch, heads, seq_q, seq_kv, dim, window_size, dtype=dtype)
+        print(f"Best latency: {kernel.latency}")
+        print(f"Best TFlops: {total_flops / kernel.latency * 1e-9}")
+        print(f"Best config: {kernel.config}")
+    else:
+        block_M = 128
+        block_N = 128
+        num_stages = 2
+        threads = 256
+        print(f"{block_M=}, {block_N=}, {num_stages=}, {threads=}")
+
+        kernel = flashattn(
+            batch,
+            heads,
+            seq_q,
+            seq_kv,
+            dim,
+            window_size,
+            block_M=block_M,
+            block_N=block_N,
+            num_stages=num_stages,
+            threads=threads,
+            dtype=dtype)
+
+        Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype)
+
+        torch.testing.assert_close(
+            kernel(Q, K, V, sinks),
+            ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype),
+            rtol=1e-2,
+            atol=1e-2)
+        print("All checks passed.✅")
+
+        latency = do_bench(
+            lambda: ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype), warmup=500)
+        print("Ref: {:.2f} ms".format(latency))
+        print("Ref: {:.2f} TFlops".format(total_flops / latency * 1e-9))
+        latency = do_bench(lambda: kernel(Q, K, V, sinks), warmup=500)
+        print("Tilelang: {:.2f} ms".format(latency))
+        print("Tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=8, help='batch size')
+    parser.add_argument('--heads', type=int, default=32, help='heads')
+    parser.add_argument('--seq_q', type=int, default=4096, help='sequence length of query')
+    parser.add_argument('--seq_kv', type=int, default=4096, help='sequence length of key/value')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument(
+        '--window_size',
+        type=int,
+        default=None,
+        help='window size (default: None, which means full attention)')
+    parser.add_argument(
+        '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16")
+    parser.add_argument('--tune', action='store_true', help='tune')
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype,
+         args.tune)

examples/attention_sink/example_mha_sink_fwd_bhsd_wgmma_pipelined.py

+# Modified from tilelang/examples/flash_attention/example_mha_fwd_bhsd_wgmma_pipelined.py
+# Optimized for Hopper architecture, with a benchmark to compare with official Triton impl
+
+import torch
+import tilelang
+from tilelang.autotuner import autotune
+from tilelang.profiler import do_bench
+import tilelang.language as T
+from tilelang.layout import make_swizzled_layout
+import itertools
+import argparse
+from typing import Optional
+
+
+def get_configs():
+    iter_params = dict(block_M=[128], block_N=[128], num_stages=[0, 1, 2], threads=[128, 256])
+    return [dict(zip(iter_params, values)) for values in itertools.product(*iter_params.values())]
+
+
+@autotune(configs=get_configs(), warmup=500, rep=100)
+@tilelang.jit(
+    out_idx=[3], pass_configs={
+        tilelang.PassConfigKey.TL_ENABLE_FAST_MATH: True,
+    })
+def flashattn(
+        batch,
+        heads,
+        seq_q,
+        seq_kv,
+        dim,
+        window_size=None,  # None for full attention
+        sm_scale=None,
+        block_M=128,
+        block_N=128,
+        num_stages=2,
+        threads=256,
+        dtype: str = "float16"):
+
+    if window_size is not None:
+        assert window_size % block_N == 0, "window_size must be divisible by block_N"
+
+    if sm_scale is None:
+        sm_scale = (1.0 / dim)**0.5
+    scale = sm_scale * 1.44269504  # log2(e)
+
+    q_shape = [batch, heads, seq_q, dim]
+    kv_shape = [batch, heads, seq_kv, dim]
+    accum_dtype = "float"
+
+    past_len = seq_kv - seq_q
+    assert past_len >= 0, "seq_kv must be greater than or equal to seq_q"
+
+    @T.macro
+    def MMA0(
+        K: T.Tensor(kv_shape, dtype),
+        Q_shared: T.SharedBuffer([block_M, dim], dtype),
+        K_shared: T.SharedBuffer([block_N, dim], dtype),
+        acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
+        k: T.int32,
+        bx: T.int32,
+        by: T.int32,
+        bz: T.int32,
+    ):
+        T.copy(K[bz, by, k * block_N:(k + 1) * block_N, :], K_shared)
+        for i, j in T.Parallel(block_M, block_N):
+            q_idx = bx * block_M + i + past_len
+            k_idx = k * block_N + j
+            if window_size is not None:
+                acc_s[i, j] = T.if_then_else(q_idx >= k_idx and q_idx < k_idx + window_size, 0,
+                                             -T.infinity(acc_s.dtype))
+            else:
+                acc_s[i, j] = T.if_then_else(q_idx >= k_idx, 0, -T.infinity(acc_s.dtype))
+        T.gemm(Q_shared, K_shared, acc_s, transpose_B=True, policy=T.GemmWarpPolicy.FullRow)
+
+    @T.macro
+    def MMA1(
+        V: T.Tensor(kv_shape, dtype),
+        V_shared: T.SharedBuffer([block_M, dim], dtype),
+        acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
+        acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+        k: T.int32,
+        by: T.int32,
+        bz: T.int32,
+    ):
+        T.copy(V[bz, by, k * block_N:(k + 1) * block_N, :], V_shared)
+        T.gemm(acc_s_cast, V_shared, acc_o, policy=T.GemmWarpPolicy.FullRow)
+
+    @T.macro
+    def Softmax(
+            acc_s: T.FragmentBuffer([block_M, block_N], accum_dtype),
+            acc_s_cast: T.FragmentBuffer([block_M, block_N], dtype),
+            scores_max: T.FragmentBuffer([block_M], accum_dtype),
+            scores_max_prev: T.FragmentBuffer([block_M], accum_dtype),
+            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
+            scores_sum: T.FragmentBuffer([block_M], accum_dtype),
+            logsum: T.FragmentBuffer([block_M], accum_dtype),
+    ):
+        T.copy(scores_max, scores_max_prev)
+        T.fill(scores_max, -T.infinity(accum_dtype))
+        T.reduce_max(acc_s, scores_max, dim=1, clear=False)
+        # To do causal softmax, we need to set the scores_max to 0 if it is -inf
+        # This process is called Check_inf in FlashAttention3 code, and it only need to be done
+        # NOTE(wt): check_inf is necessary for sliding window attention.
+        for i in T.Parallel(block_M):
+            if window_size is not None:
+                scores_max[i] = T.if_then_else(scores_max[i] == -T.infinity(accum_dtype), 0,
+                                               scores_max[i])
+            scores_scale[i] = T.exp2(scores_max_prev[i] * scale - scores_max[i] * scale)
+
+        for i, j in T.Parallel(block_M, block_N):
+            # Instead of computing exp(x - max), we compute exp2(x * log_2(e) -
+            # max * log_2(e)) This allows the compiler to use the ffma
+            # instruction instead of fadd and fmul separately.
+            acc_s[i, j] = T.exp2(acc_s[i, j] * scale - scores_max[i] * scale)
+        T.reduce_sum(acc_s, scores_sum, dim=1)
+        for i in T.Parallel(block_M):
+            logsum[i] = logsum[i] * scores_scale[i] + scores_sum[i]
+        T.copy(acc_s, acc_s_cast)
+
+    @T.macro
+    def Rescale(
+            acc_o: T.FragmentBuffer([block_M, dim], accum_dtype),
+            scores_scale: T.FragmentBuffer([block_M], accum_dtype),
+    ):
+        for i, j in T.Parallel(block_M, dim):
+            acc_o[i, j] *= scores_scale[i]
+
+    @T.prim_func
+    def main(
+            Q: T.Tensor(q_shape, dtype),
+            K: T.Tensor(kv_shape, dtype),
+            V: T.Tensor(kv_shape, dtype),
+            Output: T.Tensor(q_shape, dtype),
+            Sinks: T.Tensor([heads], dtype),
+    ):
+        with T.Kernel(T.ceildiv(seq_q, block_M), heads, batch, threads=threads) as (bx, by, bz):
+            Q_shared = T.alloc_shared([block_M, dim], dtype)
+            K_shared = T.alloc_shared([block_N, dim], dtype)
+            V_shared = T.alloc_shared([block_N, dim], dtype)
+            O_shared = T.alloc_shared([block_M, dim], dtype)
+            acc_s = T.alloc_fragment([block_M, block_N], accum_dtype)
+            acc_s_cast = T.alloc_fragment([block_M, block_N], dtype)
+            acc_o = T.alloc_fragment([block_M, dim], accum_dtype)
+            scores_max = T.alloc_fragment([block_M], accum_dtype)
+            scores_max_prev = T.alloc_fragment([block_M], accum_dtype)
+            scores_scale = T.alloc_fragment([block_M], accum_dtype)
+            scores_sum = T.alloc_fragment([block_M], accum_dtype)
+            logsum = T.alloc_fragment([block_M], accum_dtype)
+            sinks = T.alloc_fragment([block_M], dtype)
+
+            T.annotate_layout({
+                Q_shared: make_swizzled_layout(Q_shared),
+                K_shared: make_swizzled_layout(K_shared),
+                V_shared: make_swizzled_layout(V_shared),
+                O_shared: make_swizzled_layout(O_shared),
+            })
+
+            T.copy(Q[bz, by, bx * block_M:(bx + 1) * block_M, :], Q_shared)
+            T.fill(acc_o, 0)
+            T.fill(logsum, 0)
+            T.fill(scores_max, -T.infinity(accum_dtype))
+            for i in T.Parallel(block_M):
+                sinks[i] = Sinks[by]
+
+            end = T.min(
+                T.ceildiv(seq_kv, block_N), T.ceildiv((bx + 1) * block_M + past_len, block_N))
+
+            start = T.alloc_local([1], 'int32')
+            if window_size is not None:
+                start[0] = T.max(0, (bx * block_M + past_len - window_size) // block_N)
+            else:
+                start[0] = 0
+
+            for k in T.Pipelined(
+                    start[0],
+                    end,
+                    num_stages=num_stages,
+                    order=[-1, 0, 3, 1, -1, 2],
+                    stage=[-1, 0, 0, 1, -1, 1],
+                    group=[[0], [1, 2], [3, 4, 5, 6, 7, 8, 9, 10], [11], [12], [13]]):
+                MMA0(K, Q_shared, K_shared, acc_s, k, bx, by, bz)
+                Softmax(acc_s, acc_s_cast, scores_max, scores_max_prev, scores_scale, scores_sum,
+                        logsum)
+                Rescale(acc_o, scores_scale)
+                MMA1(V, V_shared, acc_s_cast, acc_o, k, by, bz)
+            for i in T.Parallel(block_M):
+                logsum[i] += T.exp2(sinks[i] * 1.44269504 -
+                                    scores_max[i] * scale)  # The only change for attention sink
+            for i, j in T.Parallel(block_M, dim):
+                acc_o[i, j] /= logsum[i]
+            T.copy(acc_o, O_shared)
+            T.copy(O_shared, Output[bz, by, bx * block_M:(bx + 1) * block_M, :])
+
+    return main
+
+
+# Following functions are adapted and optimized from
+# https://github.com/openai/gpt-oss/blob/main/gpt_oss/triton/attention.py
+def ref_program(query: torch.Tensor,
+                key: torch.Tensor,
+                value: torch.Tensor,
+                sinks: torch.Tensor,
+                sliding_window: Optional[int] = None,
+                dtype: torch.dtype = torch.float16) -> torch.Tensor:
+
+    query = query.transpose(1, 2).contiguous().unsqueeze(
+        3)  # align with the original function'sinterface
+    key = key.transpose(1, 2).contiguous()
+    value = value.transpose(1, 2).contiguous()
+
+    batch_size, num_queries, num_key_value_heads, num_key_value_groups, head_dim = query.shape
+    batch_size, num_keys, num_key_value_heads, head_dim = key.shape
+    start_q = num_keys - num_queries
+
+    sm_scale: float = 1.0 / head_dim**0.5
+
+    sinks = sinks.view(1, num_key_value_heads, num_key_value_groups, 1, 1).float()
+    key = key.unsqueeze(3)
+    value = value.unsqueeze(3)
+
+    pos_keys = torch.arange(num_keys, device=query.device)
+    pos_queries = torch.arange(num_queries, device=query.device) + start_q
+    mask = pos_keys[None, :] > pos_queries[:, None]
+    mask = mask.float().masked_fill(mask, float("-inf"))
+
+    if sliding_window:
+        too_old = pos_keys[None, :] < (pos_queries[:, None] - sliding_window + 1)
+        mask.masked_fill_(too_old, float("-inf"))
+
+    logits = torch.einsum("bqhmd,bkhmd->bhmqk", query.float(), key.float()) * sm_scale
+    logits = logits + mask[None, None, None, :, :]
+
+    logits_max = torch.max(logits, dim=-1, keepdim=True).values
+    logits_or_sinks_max = torch.maximum(sinks, logits_max)
+    sinks = torch.exp(sinks - logits_or_sinks_max)
+    unnormalized_scores = torch.exp(logits - logits_or_sinks_max)
+    normalizer = unnormalized_scores.sum(dim=-1, keepdim=True) + sinks
+    scores = unnormalized_scores / normalizer
+
+    output = torch.einsum("bhmqk,bkhmd->bqhmd", scores, value.float())
+
+    output = output.reshape(batch_size, num_queries, num_key_value_heads * num_key_value_groups,
+                            head_dim).to(dtype)
+    return output.transpose(1, 2).contiguous()
+
+
+def gen_inputs(
+        B,
+        H,
+        Sq,
+        Skv,
+        D,
+        dtype=torch.float16) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
+    query = torch.randn([B, H, Sq, D], dtype=dtype, device='cuda')
+    key = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda')
+    value = torch.randn([B, H, Skv, D], dtype=dtype, device='cuda')
+    sinks = torch.randn([H], dtype=dtype, device='cuda')
+    return query, key, value, sinks
+
+
+def main(batch: int = 1,
+         heads: int = 32,
+         seq_q: int = 256,
+         seq_kv: int = 256,
+         dim: int = 128,
+         window_size: int | None = None,
+         dtype: str = "float16",
+         tune: bool = False):
+    torch_dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[dtype]
+    if window_size is not None:
+        print('Using sliding window attention.')
+        assert window_size <= seq_q
+        flops_per_matmul = 2.0 * batch * heads * min(
+            window_size, seq_kv // 2) * seq_q * dim  # just a rough estimation
+    else:
+        print('Using full attention.')
+        flops_per_matmul = 2.0 * batch * heads * seq_q * seq_kv * dim * 0.5
+    total_flops = 2 * flops_per_matmul
+
+    if tune:
+        kernel = flashattn(batch, heads, seq_q, seq_kv, dim, window_size, dtype=dtype)
+        print(f"Best latency: {kernel.latency}")
+        print(f"Best TFlops: {total_flops / kernel.latency * 1e-9}")
+        print(f"Best config: {kernel.config}")
+    else:
+        block_M = 128
+        block_N = 128
+        num_stages = 2
+        threads = 256
+        print(f"{block_M=}, {block_N=}, {num_stages=}, {threads=}")
+
+        kernel = flashattn(
+            batch,
+            heads,
+            seq_q,
+            seq_kv,
+            dim,
+            window_size,
+            block_M=block_M,
+            block_N=block_N,
+            num_stages=num_stages,
+            threads=threads,
+            dtype=dtype)
+
+        Q, K, V, sinks = gen_inputs(batch, heads, seq_q, seq_kv, dim, dtype=torch_dtype)
+
+        torch.testing.assert_close(
+            kernel(Q, K, V, sinks),
+            ref_program(Q, K, V, sinks, window_size, dtype=torch_dtype),
+            rtol=1e-2,
+            atol=1e-2)
+        print("All checks passed.✅")
+
+        latency = do_bench(lambda: kernel(Q, K, V, sinks), warmup=500)
+        print("Tilelang: {:.2f} ms".format(latency))
+        print("Tilelang: {:.2f} TFlops".format(total_flops / latency * 1e-9))
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--batch', type=int, default=8, help='batch size')
+    parser.add_argument('--heads', type=int, default=32, help='heads')
+    parser.add_argument('--seq_q', type=int, default=4096, help='sequence length of query')
+    parser.add_argument('--seq_kv', type=int, default=4096, help='sequence length of key/value')
+    parser.add_argument('--dim', type=int, default=128, help='dim')
+    parser.add_argument(
+        '--window_size',
+        type=int,
+        default=None,
+        help='window size (default: None, which means full attention)')
+    parser.add_argument(
+        '--dtype', type=str, default="float16", help="dtype, can be float16 or bfloat16")
+    parser.add_argument('--tune', action='store_true', help='tune')
+    args = parser.parse_args()
+    main(args.batch, args.heads, args.seq_q, args.seq_kv, args.dim, args.window_size, args.dtype,
+         args.tune)

examples/attention_sink/test_example_attention_sink.py

+import tilelang.testing
+
+import example_mha_sink_fwd_bhsd
+import example_mha_sink_fwd_bhsd_wgmma_pipelined
+import example_gqa_sink_fwd_bhsd_wgmma_pipelined
+import example_mha_sink_bwd_bhsd
+import example_gqa_sink_bwd_bhsd
+
+
+@tilelang.testing.requires_cuda
+def test_example_mha_sink_fwd_bhsd_full_attn():
+    example_mha_sink_fwd_bhsd.main()
+
+
+@tilelang.testing.requires_cuda
+def test_example_mha_sink_fwd_bhsd_sliding_window():
+    example_mha_sink_fwd_bhsd.main(window_size=128)
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
+def test_example_mha_sink_fwd_bhsd_wgmma_pipelined_full_attn():
+    example_mha_sink_fwd_bhsd_wgmma_pipelined.main()
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
+def test_example_mha_sink_fwd_bhsd_wgmma_pipelined_sliding_window():
+    example_mha_sink_fwd_bhsd_wgmma_pipelined.main(window_size=128)
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
+def test_example_gqa_sink_fwd_bhsd_wgmma_pipelined_full_attn():
+    example_gqa_sink_fwd_bhsd_wgmma_pipelined.main()
+
+
+@tilelang.testing.requires_cuda
+@tilelang.testing.requires_cuda_compute_version_ge(9, 0)
+def test_example_gqa_sink_fwd_bhsd_wgmma_pipelined_sliding_window():
+    example_gqa_sink_fwd_bhsd_wgmma_pipelined.main(window_size=128)
+
+
+@tilelang.testing.requires_cuda
+def test_example_mha_sink_bwd_bhsd():
+    example_mha_sink_bwd_bhsd.main()
+
+
+@tilelang.testing.requires_cuda
+def test_example_mha_sink_bwd_bhsd_sliding_window():
+    example_mha_sink_bwd_bhsd.main(window_size=128)
+
+
+@tilelang.testing.requires_cuda
+def test_example_gqa_sink_bwd_bhsd():
+    example_gqa_sink_bwd_bhsd.main()
+
+
+@tilelang.testing.requires_cuda
+def test_example_gqa_sink_bwd_bhsd_sliding_window():
+    example_gqa_sink_bwd_bhsd.main(window_size=128)
+
+
+if __name__ == "__main__":
+    tilelang.testing.main()

examples/bitnet-1.58b/.gitignore

+models/
\ No newline at end of file

examples/bitnet-1.58b/README.md

+---
+license: mit
+---
+
+
+This is a Tilelang Implementation for the reproduced 1.58bit model from [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B). We replaced the original simulated Int8x3bit Quantized Inference Kernel with INT8xINT2 Kernel. We also evaluated the model's correctness and performance through `eval_correctness.py` and `benchmark_inference_latency.py`.
+
+## Make Checkpoints for vLLM
+
+We provide two scripts to make the checkpoints for vLLM. The first script is `generate_bitnet_model_native_format.sh`, which is used to make a checkpoint with fp16 uncompressed metaadta, the main difference with the original checkpoint is the `quant_config.json`, which allow vLLM to load the model and execute with a quant extension.
+
+```bash
+# move to the integration directory
+cd /root/to/BitBLAS/integration/BitNet
+# make the checkpoint
+./maint/generate_bitnet_model_native_format.sh
+# the output ckpy will be saved in the `./models/ckpt_bitnet_b1_58-3B` directory
+```
+
+The second script is `generate_bitnet_model_bitblas_format.sh`, which is used to make a checkpoint with BitBLAS compressed metadata, which can avoid the online dequantize sage for the profiling of vLLM, which lead to more efficient memory utilization.
+
+```bash
+./maint/generate_bitnet_model_bitblas_format.sh ./models/ckpt_bitnet_b1_58-3B ./models/ckpt_bitnet_b1_58-3B_bitblas
+# the output ckpy will be saved in the `./models/ckpt_bitnet_b1_58-3B_bitblas` directory
+```
+
+Finnaly, you can use the ckpt in vLLM with:
+
+```bash
+cd vllm_workspace
+# inference with the ckpt with fp16 uncompressed metadata
+python3 inference_with_native_format.py
+# inference with the ckpt with BitBLAS compressed metadata
+python3 inference_with_bitblas_format.py
+```
+
+**Benchmark results of vLLM**
+
+| Model                  | Framework                | BS16IN32OUT128 | BS1IN512OUT1024 | BS32IN32OUT128 |
+|------------------------|--------------------------|----------------|-----------------|----------------|
+| bitnet-3b-1.58bits     | pytorch                  | 106.83         | 49.34           | 209.03         |
+| bitnet-3b-1.58bits     | pytorch-tilelang         | 240.33         | 103.09          | 493.31         |
+| bitnet-3b-1.58bits     | vllm-tilelang            | 379.25         | 117.43          | 752.55         |
+| bitnet-3b-1.58bits     | vllm-tilelang-cuda-graph | 2543.58        | 1621.08         | 2731.79        |
+
+
+## BitBLAS Results
+
+### Performance
+
+**Note:** To reproduce the results of BitBLAS, Please checkout the `benchmark_inference_latency.py`. To reproduce the results of the original model, Please checkout the [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B) repo.
+
+|      Model      | Device | batchsize | in_seq |   model  | bitnet-1.58b-3b-huggingface | bitnet-1.58b-3b-bitblas |
+|:---------------:|:------:|:---------:|:------:|:--------:|:---------------------------:|:-----------------------:|
+| bitnet_b1_58-3B |  A100  |     1     |    1   | LLAMA-3B |         177.6729107         |       64.17962909       |
+| bitnet_b1_58-3B |  A100  |    128    |    1   | LLAMA-3B |         188.6145592         |       63.48158518       |
+| bitnet_b1_58-3B |  A100  |     1     |  2048  | LLAMA-3B |         348.7066031         |       202.6877999       |
+
+### On-the-Fly GPU Memory Footprint
+
+We measured the GPU memory footprint through the `nvidia-smi` command. Please checkout `nvidia_measure_memory.sh` to get the real-time GPU memory usage. And then start a `benchmark_model_10k_loops.py` workload to measure the overall GPU memory usage.
+
+|    **Model**    | **Device** | **batchsize** | **in_seq** | **bitnet-1.58b-3b-huggingface** | **bitnet-1.58b-3b-bitblas** |
+|:---------------:|:----------:|:-------------:|:----------:|:-------------------------------:|:---------------------------:|
+| bitnet_b1_58-3B |    A100    |       1       |      1     |             7595 MB             |           1729 MB           |
+| bitnet_b1_58-3B |    A100    |      128      |      1     |             7677 MB             |           1789 MB           |
+| bitnet_b1_58-3B |    A100    |       1       |    2048    |             8731 MB             |           3163 MB           |
+
+## PPL and Zero-shot Accuracy
+
+The number is Reported from the [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B), Please checkout the `eval_ppl.py`.
+
+PPL and zero-shot accuracy:
+| Models | PPL| ARCe| ARCc| HS | BQ | OQ | PQ | WGe | Avg
+|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
+| FP16 700M (reported) | 12.33 | 54.7 | 23.0 | 37.0 | 60.0 | 20.2 | 68.9 | 54.8 | 45.5 |
+| BitNet b1.58 700M (reported) | 12.87 | 51.8 | 21.4 | 35.1 | 58.2 | 20.0 | 68.1 | 55.2 | 44.3 |
+| BitNet b1.58 700M (reproduced) | 12.78 | 51.4 | 21.8 | 35.0 | 59.6 | 20.6 | 67.5 | 55.4 | 44.5 |
+| FP16 1.3B (reported)    | 11.25  | 56.9 | 23.5 | 38.5 | 59.1 | 21.6 | 70.0 | 53.9 | 46.2
+| BitNet b1.58 1.3B (reported)    | 11.29  | 54.9 | 24.2 | 37.7 | 56.7 | 19.6 | 68.8 | 55.8 | 45.4 |
+| BitNet b1.58 1.3B (reproduced)    | 11.19 | 55.8 | 23.7 | 37.6 | 59.0 | 20.2 | 69.2 | 56.0 | 45.9
+| FP16 3B (reported)    | 10.04   | 62.1 | 25.6 | 43.3 | 61.8 | 24.6 | 72.1 | 58.2 | 49.7
+| BitNet b1.58 3B (reported)    | 9.91   | 61.4 | 28.3 | 42.9 | 61.5 | 26.6 | 71.5 | 59.3 | 50.2
+| BitNet b1.58 3B (reproduced)    | 9.88 | 60.9 | 28.0 | 42.3 | 58.3 | 26.0 | 71.4 | 60.3 | 49.6 |
+
+The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors.
+
+## Citations
+
+```bibtex
+@article{ma2024era,
+  title={The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits},
+  author={Ma, Shuming and Wang, Hongyu and Ma, Lingxiao and Wang, Lei and Wang, Wenhui and Huang, Shaohan and Dong, Li and Wang, Ruiping and Xue, Jilong and Wei, Furu},
+  journal={arXiv preprint arXiv:2402.17764},
+  year={2024}
+}
+```
\ No newline at end of file

examples/bitnet-1.58b/benchmark.sh

+python benchmark_generate.py --bs 16 --in_seq_len 32 --out_seq_len 128 | tee b16_i32_o128.log
+
+python benchmark_generate.py --bs 1 --in_seq_len 512 --out_seq_len 64 | tee b1_i512_o64.log
+
+python benchmark_generate.py --bs 32 --in_seq_len 32 --out_seq_len 128 | tee b32_i32_o128.log
+
+python benchmark_generate.py --bs 16 --in_seq_len 32 --out_seq_len 128 --bitblas | tee b16_i32_o128_bitblas.log
+
+python benchmark_generate.py --bs 1 --in_seq_len 512 --out_seq_len 64 --bitblas | tee b1_i512_o64_bitblas.log
+
+python benchmark_generate.py --bs 32 --in_seq_len 32 --out_seq_len 128 --bitblas | tee b32_i32_o128_bitblas.log

examples/bitnet-1.58b/benchmark_generate.py

+import torch
+import bitblas
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+from transformers import GenerationConfig
+import time
+import argparse
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+
+def generate_text_batch(model, tokenizer, prompts, max_length=100):
+    # Encode the input prompts as a batch
+    input_ids = tokenizer(
+        prompts, return_tensors="pt", padding=True, truncation=True).input_ids.to(model.device)
+
+    # Generate cos and sin values (commented out as not used in generation)
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+    # position_embeddings = model.embed_positions(position_ids)
+    # cos = position_embeddings[:, :, 0::2].cos()
+    # sin = position_embeddings[:, :, 1::2].sin()
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    # output_ids = model.generate(input_ids, generation_config=generation_config, cos=cos, sin=sin)
+    end_time = time.time()
+
+    # Decode the output ids to text
+    generated_texts = [
+        tokenizer.decode(output_id, skip_special_tokens=True) for output_id in output_ids
+    ]
+
+    generation_time = end_time - start_time
+    num_tokens = sum(len(output_id) for output_id in output_ids)
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_texts
+
+
+def profile(model, input_data):
+
+    import numpy as np
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+model_path = '1bitLLM/bitnet_b1_58-3B'
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--bs', default=16, type=int)
+    parser.add_argument('--in_seq_len', default=32, type=int)
+    parser.add_argument('--out_seq_len', default=128, type=int)
+    parser.add_argument('--bitblas', action='store_true')
+    args = parser.parse_args()
+    bs = args.bs
+    in_seq_len = args.in_seq_len
+    out_seq_len = args.out_seq_len
+    is_bitblas = args.bitblas
+    model = BitnetForCausalLM.from_pretrained(
+        model_path,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).cuda().half()
+    if is_bitblas:
+        with torch.no_grad():
+            model.quantize()
+
+    tokenizer = BitnetTokenizer.from_pretrained(model_path)
+    prompt = ""
+    for _ in range(in_seq_len):
+        prompt += "Hello "
+
+    prompts = []
+    for _ in range(bs):
+        prompts.append(prompt)
+    max_length = out_seq_len + in_seq_len
+    print(generate_text_batch(model, tokenizer, prompts, max_length=max_length))
+
+
+if __name__ == '__main__':
+    main()

examples/bitnet-1.58b/benchmark_inference_latency.py

+import argparse
+import torch
+
+from modeling_bitnet import BitnetForCausalLM
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
+
+
+def profile(model, input_data):
+    import time
+
+    import numpy as np
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        '1bitLLM/bitnet_b1_58-3B',
+        device_map='auto',
+        low_cpu_mem_usage=True,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    with torch.no_grad():
+        model.quantize()
+        model = torch.compile(model)
+
+    benchmark_sets = [(1024, 1), (1, 2048)]
+    for batch_size, seq_len in benchmark_sets:
+        input_id = torch.ones(batch_size, seq_len).long().cuda()
+        latency = profile(model, input_id)
+        print(f"Batch size: {batch_size}, Seq len: {seq_len}, Latency: {latency}")
+
+
+if __name__ == '__main__':
+    main()

examples/bitnet-1.58b/benchmark_model_10k_loops.py

+import argparse
+import torch
+
+from modeling_bitnet import BitnetForCausalLM
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--hf_path", default="1bitLLM/bitnet_b1_58-3B", type=str)
+parser.add_argument("--batch_size", default=1, type=int)
+parser.add_argument("--seq_len", default=1, type=int)
+
+args = parser.parse_args()
+
+seq_len = args.seq_len
+batch_size = args.batch_size
+
+
+def profile(model, input_data):
+    import time
+
+    import numpy as np
+
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        "1bitLLM/bitnet_b1_58-3B",
+        device_map="auto",
+        low_cpu_mem_usage=True,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    with torch.no_grad():
+        model._post_process_weights()
+
+    torch.cuda.empty_cache()
+
+    input_id = torch.ones(batch_size, seq_len).long().cuda()
+    for _ in range(10000):
+        _ = model(input_id)
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/configuration_bitnet.py

+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
+#
+# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
+# and OPT implementations in this library. It has been modified from its
+# original forms to accommodate minor architectural differences compared
+# to GPT-NeoX and OPT used by the Meta AI team that trained the model.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" LLaMA model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+logger = logging.get_logger(__name__)
+
+LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class BitnetConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`BitnetModel`]. It is used to instantiate an LLaMA
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the LLaMA-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`BitnetModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. Bitnet 1 supports up to 2048 tokens,
+            Bitnet 2 up to 4096, CodeBitnet up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import BitnetModel, BitnetConfig
+
+    >>> # Initializing a LLaMA llama-7b style configuration
+    >>> configuration = BitnetConfig()
+
+    >>> # Initializing a model from the llama-7b style configuration
+    >>> model = BitnetModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "llama"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=False,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        weight_bits=1,
+        input_bits=8,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.weight_bits = weight_bits
+        self.input_bits = input_bits
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}")
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor,
+                                                         float) or rope_scaling_factor <= 1.0:
+            raise ValueError(
+                f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")

examples/bitnet-1.58b/eval_correctness.py

+import torch
+import bitblas
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+from transformers import GenerationConfig
+import time
+import transformers
+
+print(f"transformers version is {transformers.__version__}")
+
+# version must be lower than or equal to 4.40
+assert transformers.__version__ <= "4.40.0"
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+
+def generate_text(model, tokenizer, prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.lm_head.weight.device)
+    # Generate cos and sin values
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    end_time = time.time()
+
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    generation_time = end_time - start_time
+    num_tokens = len(output_ids[0])
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_text
+
+
+def profile(model, input_data):
+
+    import numpy as np
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+model_path = '1bitLLM/bitnet_b1_58-3B'
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        model_path,
+        use_flash_attention_2=False,
+        torch_dtype=torch.float16,
+    ).cuda().half()
+
+    tokenizer = BitnetTokenizer.from_pretrained(model_path, use_fast=False)
+    input_id = tokenizer("Hello")['input_ids']
+    input_id = torch.tensor(input_id).unsqueeze(0).cuda()
+
+    print("original model generated text:")
+    print(generate_text(model, tokenizer, "Hello", max_length=100))
+
+    model.quantize()
+    print("quantized model generated text:")
+    print(generate_text(model, tokenizer, "Hello", max_length=100))
+
+
+if __name__ == '__main__':
+    main()

examples/bitnet-1.58b/eval_gpu_memory.py

+import argparse
+import torch
+
+from modeling_bitnet import BitnetForCausalLM
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
+
+
+def profile(model, input_data):
+    import time
+
+    import numpy as np
+    model = model.cuda()
+    model.eval()
+
+    def get_runtime(num_repeats=1):
+        tic = time.time()
+        for _ in range(num_repeats):
+            _ = model(input_data)
+        torch.cuda.synchronize()
+        return (time.time() - tic) * 1000 / num_repeats
+
+    with torch.no_grad():
+        st = time.time()
+        while time.time() - st < 1.0:
+            get_runtime()  # warmup
+        warmup_runtime = get_runtime()
+        num_repeats = max(1, int(1000 / warmup_runtime))
+        times = get_runtime(num_repeats)
+    return np.mean(times)
+
+
+def main():
+    model = BitnetForCausalLM.from_pretrained(
+        '1bitLLM/bitnet_b1_58-3B',
+        device_map='auto',
+        low_cpu_mem_usage=True,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).half()
+    print(f"gpu memory: {torch.cuda.memory_allocated() / 1024 ** 3} GB")
+    with torch.no_grad():
+        model._post_process_weights()
+    print(f"gpu memory BitBLAS: {torch.cuda.memory_allocated() / 1024 ** 3} GB")
+
+
+if __name__ == '__main__':
+    main()

examples/bitnet-1.58b/eval_ppl.py

+# pylint: disable=missing-docstring, invalid-name
+"""This is modified from https://huggingface.co/1bitLLM/bitnet_b1_58-3B/blob/main/utils_quant.py."""
+
+import math
+import argparse
+import torch
+import random
+
+from eval_utils import get_test_dataset
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+
+from tqdm import tqdm
+
+torch.set_grad_enabled(False)
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--seed', default=0, type=int)
+parser.add_argument('--hf_path', default='1bitLLM/bitnet_b1_58-3B', type=str)
+parser.add_argument('--seqlen', default=2048, type=int)
+
+
+def calulate_loss(model, input, loss_fct):
+    output = model(input, use_cache=False, output_hidden_states=False, output_attentions=False)[0]
+    shift_logits = output[:, :-1, :].contiguous()
+    shift_labels = input[:, 1:]
+    loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
+    return loss
+
+
+def main(args):
+    datasets = ['c4', 'wikitext2']
+    model = BitnetForCausalLM.from_pretrained(
+        args.hf_path,
+        use_flash_attention_2=True,
+        torch_dtype=torch.float16,
+    ).cuda().half()
+    with torch.no_grad():
+        model._post_process_weights()
+    tokenizer = BitnetTokenizer.from_pretrained(args.hf_path, use_fast=False)
+    loss_fct = torch.nn.CrossEntropyLoss(reduction="sum").cuda()
+
+    ppl = []
+    for dataset in datasets:
+        testdata = get_test_dataset(dataset, tokenizer, seqlen=args.seqlen)
+        acc_loss, count = 0.0, 0
+        progress = tqdm(range(len(testdata)))
+        for ii in progress:
+            input = torch.Tensor(testdata[ii]).long().cuda().view(1, -1)
+            loss = calulate_loss(model, input, loss_fct)
+            count += (input.size(-1) - 1)
+            acc_loss += loss.item()
+            progress.set_description(f"avg_loss = {acc_loss/ count / math.log(2)}")
+
+        avg_loss = acc_loss / count / math.log(2)
+        ppl.append(2**avg_loss)
+        print("{} PPL: {}".format(dataset, ppl[-1]))
+
+    print(ppl)
+    print("Avg PPL:", sum(ppl) / len(ppl))
+
+
+if __name__ == '__main__':
+    torch.set_grad_enabled(False)
+    args = parser.parse_args()
+    random.seed(args.seed)
+    torch.random.manual_seed(args.seed)
+    main(args)

examples/bitnet-1.58b/eval_utils.py

+# ruff: noqa
+import torch
+
+import numpy as np
+import torch.nn.functional as F
+
+from lm_eval.base import BaseLM
+from datasets import load_dataset
+
+
+def set_seed(seed):
+    np.random.seed(seed)
+    torch.random.manual_seed(seed)
+
+
+def get_test_dataset(dataset_name, tokenizer, seqlen=2048):
+    if dataset_name == "wikitext2":
+        testdata = load_dataset('wikitext', 'wikitext-2-raw-v1', split='test')
+        testdata = "".join(testdata['text']).split('\n')
+    elif dataset_name == "c4":
+        testdata = load_dataset(
+            'allenai/c4',
+            data_files={'validation': 'en/c4-validation.00000-of-00008.json.gz'},
+            split='validation')['text']
+    else:
+        raise NotImplementedError
+
+    testdata = [item for item in testdata if item != ""]
+    tokenized_text = [
+        tokenizer(item, add_special_tokens=False)['input_ids'] + [tokenizer.eos_token_id]
+        for item in testdata
+    ]
+
+    data, doc = [], [tokenizer.bos_token_id]
+    for sen in tokenized_text:
+        if len(sen) > seqlen:
+            continue
+        if len(doc) + len(sen) > seqlen:
+            data.append(doc)
+            doc = [tokenizer.bos_token_id]
+        doc.extend(sen)
+    if len(doc) > 1 and len(doc) <= seqlen:
+        data.append(doc)
+    return data
+
+
+class LMEvalAdaptor(BaseLM):
+
+    def __init__(self, model_name, model, tokenizer, batch_size=1, max_length=-1):
+        super().__init__()
+
+        assert isinstance(batch_size, int)
+
+        self.model_name = model_name
+        self.model = model
+        self.model.eval()
+
+        self.tokenizer = tokenizer
+
+        self.vocab_size = self.tokenizer.vocab_size
+
+        self._batch_size = batch_size
+
+        self._max_length = max_length
+
+    @property
+    def eot_token_id(self):
+        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
+        return self.tokenizer.eos_token_id
+
+    @property
+    def max_length(self):
+        if self._max_length != -1:
+            return self._max_length
+        if hasattr(self.model.config, "n_ctx"):
+            return self.model.config.n_ctx
+        elif hasattr(self.model.config, "max_position_embeddings"):
+            return self.model.config.max_position_embeddings
+        elif hasattr(self.model.config, "n_positions"):
+            return self.model.config.n_positions
+        elif "bloom" in self.model_name:
+            return 2048
+        elif "llama" in self.model_name:
+            return 2048  # TODO: did not check this
+        elif "mpt" in self.model_name:
+            return 2048
+        elif "falcon" in self.model_name:
+            return 2048
+        else:
+            print(self.model.config)
+            raise NotImplementedError
+
+    @property
+    def max_gen_toks(self):
+        return 256
+
+    @property
+    def batch_size(self):
+        return self._batch_size
+
+    @property
+    def device(self):
+        return "cuda"
+
+    def tok_encode(self, string: str, add_special_tokens=True):
+        return self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
+
+    def tok_decode(self, tokens):
+        return self.tokenizer.decode(tokens)
+
+    def loglikelihood(self, requests):
+        new_reqs = []
+        for context, continuation in requests:
+            context, continuation = context.strip(), continuation.strip()
+            if context == "":
+                # end of text as context
+                context_enc = [self.eot_token_id]
+            else:
+                context_enc = self.tok_encode(context, add_special_tokens=True)
+
+            continuation_enc = self.tok_encode(continuation, add_special_tokens=False)
+
+            new_reqs.append(((context, continuation), context_enc, continuation_enc))
+
+        return self._loglikelihood_tokens(new_reqs)
+
+    def _model_call(self, inps):
+        """
+        inps: a torch tensor of shape [batch, sequence]
+        the size of sequence may vary from call to call
+
+        returns: a torch tensor of shape [batch, sequence, vocab] with the
+        logits returned from the model
+        """
+        with torch.no_grad():
+            out = self.model(inps)[0]
+        return out
+
+    def _model_generate(self, context, max_length, eos_token_id):
+        return self.model.generate(
+            context, max_length=max_length, eos_token_id=eos_token_id, do_sample=False)

examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_decode.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch
+import torch.backends
+import tilelang
+import tilelang.language as T
+from tilelang import tvm as tvm
+from tvm import DataType
+import numpy as np
+
+from tilelang.transform import simplify_prim_func
+
+torch.manual_seed(42)
+
+decode_i2s_to_i8s = """template <typename T1, typename T2>
+__device__ void decode_i2s_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+    static constexpr uint MEDIAN_NUM = 0x02020202;
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+        i8s[i] = __vsub4(i8s[i], MEDIAN_NUM);
+    }
+}
+template <typename T1, typename T2>
+__device__ void decode_i2u_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+    }
+}
+"""
+
+
+@simplify_prim_func
+def bitnet_158_int8xint2_decode(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+    fast_decoding=True,
+    n_partition=4,
+    reduce_thread=32,
+):
+    assert in_dtype in [
+        "float16",
+        "int8",
+    ], "Currently only float16 and int8 are supported"
+    assert out_dtype in [
+        "float16",
+        "float32",
+        "int32",
+    ], "Currently only float16, float32 and int32 are supported"
+    storage_nbit = 8
+    num_bits = 2
+    A_shape = (M, K)
+    B_shape = (N, K // storage_nbit * num_bits)
+    C_shape = (M, N)
+
+    num_elems_per_byte = 4
+    MAX_TRANSACTION_SIZE_IN_BITS = 128
+    micro_size_k = MAX_TRANSACTION_SIZE_IN_BITS // DataType(in_dtype).bits
+    micro_size_k_compressed = micro_size_k // num_elems_per_byte
+    storage_dtype = "int8"
+    block_K = reduce_thread * micro_size_k
+
+    use_dp4a = True
+    dp4a_size = 4
+
+    @T.prim_func
+    def kernel(
+            A: T.Buffer(A_shape, in_dtype),
+            B: T.Buffer(B_shape, storage_dtype),
+            C: T.Buffer(C_shape, out_dtype),
+    ):
+        with T.Kernel(
+                T.ceildiv(N, n_partition),
+                M,
+                threads=(reduce_thread, n_partition),
+        ) as (
+                bx,
+                by,
+        ):
+            A_local = T.alloc_local((micro_size_k,), in_dtype)
+            B_quant_local = T.alloc_local([micro_size_k_compressed], storage_dtype)
+            B_dequantize_local = T.alloc_local([micro_size_k], in_dtype)
+            accum_res = T.alloc_local((1,), accum_dtype)
+            reduced_accum_res = T.alloc_local((1,), accum_dtype)
+
+            kr = T.thread_binding(0, reduce_thread, thread="threadIdx.x")
+            ni = T.thread_binding(0, n_partition, thread="threadIdx.y")
+
+            T.import_source(decode_i2s_to_i8s)
+
+            T.clear(accum_res)
+            for ko in T.serial(T.ceildiv(K, block_K)):
+                for v in T.vectorized(micro_size_k):
+                    A_local[v] = A[by, ko * block_K + kr * micro_size_k + v]
+
+                for v in T.vectorized(micro_size_k_compressed):
+                    B_quant_local[v] = B[
+                        bx * n_partition + ni,
+                        ko * (reduce_thread * micro_size_k_compressed) +
+                        kr * micro_size_k_compressed + v,
+                    ]
+
+                T.call_extern(
+                    "handle",
+                    "decode_i2u_to_i8s",
+                    T.address_of(B_quant_local[0]),
+                    T.address_of(B_dequantize_local[0]),
+                )
+
+                if use_dp4a:
+                    for ki in T.serial(micro_size_k // dp4a_size):
+                        T.dp4a(
+                            A_local[ki * dp4a_size],
+                            B_dequantize_local[ki * dp4a_size],
+                            accum_res[0],
+                        )
+                else:
+                    for ki in T.serial(micro_size_k):
+                        accum_res[0] += A_local[ki] * B_dequantize_local[ki]
+
+            with T.attr(
+                    T.comm_reducer(lambda x, y: x + y, [T.Cast(accum_dtype, 0)]),
+                    "reduce_scope",
+                    T.reinterpret(T.uint64(0), dtype="handle"),
+            ):
+                T.evaluate(
+                    T.tvm_thread_allreduce(
+                        T.uint32(1),
+                        accum_res[0],
+                        True,
+                        reduced_accum_res[0],
+                        kr,
+                        dtype="handle",
+                    ))
+            if kr == 0:
+                C[by, bx * n_partition + ni] = reduced_accum_res[0]
+
+    return kernel
+
+
+def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8):
+    elems_per_byte = 8 // source_bits
+    if lowprecision_weight.dtype == np.float16:
+        lowprecision_weight = lowprecision_weight.astype(dtype=np.int8)
+    int8_weight = np.zeros(
+        (
+            *lowprecision_weight.shape[:-1],
+            lowprecision_weight.shape[-1] // elems_per_byte,
+        ),
+        dtype=np.int8,
+    )
+    for j in range(lowprecision_weight.shape[-1] // elems_per_byte):
+        for k in range(elems_per_byte):
+            int8_weight[:, j] |= lowprecision_weight[:, j * elems_per_byte + k] << (source_bits * k)
+
+    return int8_weight.view(storage_dtype)
+
+
+# interleave weight numpy implementation
+def interleave_weight(qweight, nbits=4, target_dtype="float16"):
+    assert target_dtype in ["float16", "int8"]
+    # reinterpret the data type of qweight to int32
+    qweight = qweight.view(np.int32)
+    new_qweight = np.zeros_like(qweight)
+    bits_stride = 8 if target_dtype == "int8" else 16
+    mask = (1 << nbits) - 1  # for 4bit the val is 0x0000000f
+    num_groups = 32 // bits_stride
+    elems_per_group = bits_stride // nbits
+    for i in range(num_groups):
+        for j in range(elems_per_group):
+            offset = i * elems_per_group + j
+            shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits
+            new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift
+
+    if nbits == 1 and target_dtype == "int8":
+        # special handling for 1b interleave
+        n16_weight = new_qweight & np.int32(0xF0F00F0F)
+        n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16
+        n16_weight |= ((new_qweight & np.int32(0x0000F000)) >> 12) << 24
+        n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4
+        n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12
+        return n16_weight.view(np.int8)
+    elif nbits == 2 and target_dtype == "float16":
+        n8_weight = new_qweight & np.int32(0xFF0000FF)
+        n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16
+        n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8
+        return n8_weight.view(np.int8)
+    elif nbits == 1 and target_dtype == "float16":
+        n8_weight = new_qweight & 0xF000000F
+        n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8
+        n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16
+        n8_weight |= ((new_qweight & 0x0000F000) >> 12) << 24
+        n8_weight |= ((new_qweight & 0x000F0000) >> 16) << 4
+        n8_weight |= ((new_qweight & 0x00F00000) >> 20) << 12
+        n8_weight |= ((new_qweight & 0x0F000000) >> 24) << 20
+
+    return new_qweight.view(np.int8)
+
+
+def assert_bitnet_158_int8xint2_decode_correctness(M,
+                                                   N,
+                                                   K,
+                                                   in_dtype,
+                                                   out_dtype,
+                                                   accum_dtype,
+                                                   fast_decoding=True):
+    program = bitnet_158_int8xint2_decode(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding)
+    print(program)
+    kernel = tilelang.compile(program)
+    src_code = kernel.get_kernel_source()
+    # src_code is the generated cuda source
+    assert src_code is not None
+    print(src_code)
+    A = torch.randint(0, 4, (M, K), device="cuda", dtype=getattr(torch, in_dtype))
+    B = torch.randint(0, 2, (N, K), device="cuda", dtype=getattr(torch, in_dtype))
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    qw = general_compress(B.cpu().numpy(), source_bits=2, storage_dtype=np.int8)
+    qw = interleave_weight(qw, 2, target_dtype=in_dtype)
+    qw = torch.from_numpy(qw).to(device="cuda")
+
+    kernel(A, qw, C)
+    # Get Reference Result
+    ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, accum_dtype))
+
+    print(ref_c)
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+    assert_bitnet_158_int8xint2_decode_correctness(1, 256, 256, "int8", "int32", "int32")

examples/bitnet-1.58b/kernel_benchmark/tilelang_bitnet_158_int8xint2_prefill.py

+# Copyright (c) Microsoft Corporation.
+# Licensed under the MIT License.
+
+import torch
+import torch.backends
+import tilelang
+import tilelang.language as T
+from tilelang import tvm as tvm
+from tvm import DataType
+from tilelang.intrinsics.mma_layout import (
+    make_mma_swizzle_layout as make_swizzle_layout,)
+import numpy as np
+
+from tilelang.intrinsics.mma_macro_generator import (
+    INT4TensorCoreIntrinEmitter,)
+from tilelang.transform import simplify_prim_func
+
+torch.manual_seed(42)
+
+decode_i2s_to_i8s = """template <typename T1, typename T2>
+__device__ void decode_i2s_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+    static constexpr uint MEDIAN_NUM = 0x02020202;
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+        i8s[i] = __vsub4(i8s[i], MEDIAN_NUM);
+    }
+}
+template <typename T1, typename T2>
+__device__ void decode_i2u_to_i8s(T1 *_i2b, T2 *_i8s, const int N = 16)
+{
+    // convert 8 int2b_t to 8 int8b_t -> 2 int32
+    uint *i8s = reinterpret_cast<uint *>(_i8s);
+
+    // i2b = {e7,e6,e5,e4,e3,e2,e1,e0}
+    // also require interleave {e7,e3,e6,e2,e5,e1,e4,e0}
+    uint const i2b = *reinterpret_cast<uint *>(_i2b);
+
+    // First, we extract the i4s and construct an intermediate fp16 number.
+    static constexpr uint immLut = (0xf0 & 0xcc) | 0xaa; // 0b11101010
+    static constexpr uint BOTTOM_MASK = 0x03030303;      // 0xf -> 0b11 select 0,3
+    static constexpr uint I8s_MAGIC_NUM = 0x00000000;    // 1024
+
+#pragma unroll
+    for (int i = 0; i < (N / 4); i++)
+    {
+        asm volatile("lop3.b32 %0, %1, %2, %3, %4;\\n"
+                     : "=r"(i8s[i])
+                     : "r"(i2b >> (2 * i)), "n"(BOTTOM_MASK), "n"(I8s_MAGIC_NUM), "n"(immLut));
+    }
+}
+"""
+
+
+@simplify_prim_func
+def bitnet_158_int8xint2_prefill(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+    fast_decoding=True,
+    block_row_warps=2,
+    block_col_warps=2,
+    warp_row_tiles=32,
+    warp_col_tiles=32,
+    chunk=64,
+):
+    """
+    Create a TVM GPU prim_func implementing a block-tiled matrix multiply that multiplies dense A by compressed/interleaved low‑precision B (2-bit packed into int8 storage), decoding B to int8 on-chip and accumulating into C.
+
+    The returned prim_func expects:
+    - A: shape (M, K) with dtype `in_dtype` ("float16" or "int8").
+    - B: compressed storage with shape (N, K/4) and int8 storage layout (packing 4 2-bit elements per byte).
+    - C: output buffer shape (M, N) with dtype `out_dtype` ("float16", "float32", or "int32").
+
+    Details:
+    - Builds a tiled, pipelined kernel using shared memory and warp-level MMA intrinsics (INT4TensorCoreIntrinEmitter). B is loaded from compressed storage, decoded to int8 in threads (via decode_i2u_to_i8s / decode_i2s_to_i8s), and dequantized into a shared buffer used by the MMA emitter.
+    - Tiling parameters:
+      - block_row_warps, block_col_warps: number of warps per block in row/col.
+      - warp_row_tiles, warp_col_tiles: tiles per warp.
+      - chunk: K-sized chunk per block (block_K).
+      - micro sizes are fixed (16x16x16, except micro_k=32 when accum_dtype == "int32").
+    - Uses 2-stage pipelining by default to overlap loads and compute and applies a swizzle layout to improve L2 behavior.
+    - Assertions: raises AssertionError if in_dtype or out_dtype are not among supported values.
+
+    Parameters:
+        M, N, K (int): Global matrix dimensions.
+        in_dtype (str): Input and decoded B element dtype; "float16" or "int8".
+        out_dtype (str): Output C dtype; one of "float16", "float32", "int32".
+        accum_dtype (str): Accumulator dtype used by MMA (e.g., "int32").
+        fast_decoding (bool): If True, enable the fast decoding path (affects which device decode is used).
+        block_row_warps (int): Warps in block row dimension.
+        block_col_warps (int): Warps in block column dimension.
+        warp_row_tiles (int): Tiles per warp in row dimension.
+        warp_col_tiles (int): Tiles per warp in column dimension.
+        chunk (int): K-length per block (block_K).
+
+    Returns:
+        T.prim_func: A TVM prim_func implementing the described GPU kernel suitable for compilation and execution.
+    """
+    assert in_dtype in [
+        "float16",
+        "int8",
+    ], "Currently only float16 and int8 are supported"
+    assert out_dtype in [
+        "float16",
+        "float32",
+        "int32",
+    ], "Currently only float16, float32 and int32 are supported"
+
+    micro_size_x = micro_size_y = micro_size_k = 16
+
+    if accum_dtype == "int32":
+        micro_size_k = 32
+
+    num_elems_per_byte = 4
+    MAX_TRANSACTION_SIZE_IN_BITS = 128
+    local_size = MAX_TRANSACTION_SIZE_IN_BITS // DataType(in_dtype).bits
+    local_size_compressed = local_size // num_elems_per_byte
+
+    shared_scope = "shared.dyn"
+    storage_dtype = "int8"
+
+    # Pipeline Stage
+    stage = 2
+
+    block_M = block_row_warps * warp_row_tiles
+    block_N = block_col_warps * warp_col_tiles
+    block_K = chunk
+
+    A_shape = (M, K)  # int8 storage represents int4*2
+    B_shape = (N, K // num_elems_per_byte)  # int8 storage represents int4*2
+    A_shared_shape = (block_M, block_K)
+    B_shared_shape = (block_N, block_K // num_elems_per_byte)
+    B_dequantize_shared_shape = (block_N, block_K)
+    C_shared_shape = (
+        block_M // micro_size_x,
+        block_N // micro_size_y,
+        micro_size_x,
+        micro_size_y,
+    )
+
+    warp_size = 32
+    threads = warp_size * (block_row_warps * block_col_warps)
+    fragement_size_a = (micro_size_x * micro_size_k) // warp_size
+    fragement_size_b = (micro_size_y * micro_size_k) // warp_size
+    fragement_size_c = (micro_size_x * micro_size_y) // warp_size
+    warp_rows = warp_row_tiles // micro_size_x
+    warp_cols = warp_col_tiles // micro_size_y
+
+    # MMA Wrapper to Auto Generate Code for MMA
+    mma_emitter = INT4TensorCoreIntrinEmitter(
+        a_dtype=in_dtype,
+        b_dtype=in_dtype,
+        accum_dtype=accum_dtype,
+        a_transposed=False,
+        b_transposed=True,
+        block_row_warps=block_row_warps,
+        block_col_warps=block_col_warps,
+        warp_row_tiles=warp_row_tiles,
+        warp_col_tiles=warp_col_tiles,
+        chunk=chunk,
+    )
+
+    @T.prim_func
+    def main(
+            A: T.Buffer(A_shape, in_dtype),
+            B: T.Buffer(B_shape, storage_dtype),
+            C: T.Buffer((M, N), out_dtype),
+    ):
+        """
+            GPU kernel entry that performs a blocked, pipelined matrix multiplication A @ B.T writing into C.
+
+            This kernel:
+            - Loads tiles of A and a compressed/interleaved representation of B from global memory into shared memory.
+            - Decodes B's packed low-precision format (storage_dtype, e.g., 2-bit packed) into element values of `in_dtype` in shared memory via an external decode routine.
+            - Uses Warp/MMA tiled fragments and an INT4/INT2-capable MMA emitter to compute accumulation across K in a pipelined fashion with configurable stages.
+            - Writes accumulated tile results from shared memory back to global C with the expected block/micro-tile indexing.
+
+            Parameters:
+                A: Input matrix buffer of shape A_shape and element type `in_dtype`. Represents the MxK activations.
+                B: Compressed/interleaved weight buffer of shape B_shape and storage type `storage_dtype`. Must contain B in the packed low-precision layout expected by the decode routine used by this kernel.
+                C: Output buffer of shape (M, N) and type `out_dtype`; receives the resulting matrix (accumulated values are produced in `accum_dtype` and stored into C).
+
+            Side effects:
+                Writes results into C. Calls external device decode functions to expand B from its packed representation into shared memory before computation.
+        """
+        with T.Kernel(
+                T.ceildiv(N, block_N),
+                T.ceildiv(M, block_M),
+                threads=threads,
+                prelude=decode_i2s_to_i8s,
+        ) as (bx, by):
+
+            A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, storage_dtype, scope=shared_scope)
+            B_dequantize_shared = T.alloc_shared(
+                B_dequantize_shared_shape, in_dtype, scope=shared_scope)
+            C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
+            A_frag = T.alloc_local((warp_rows * fragement_size_a), in_dtype)
+            B_frag = T.alloc_local((warp_cols * fragement_size_b), in_dtype)
+            C_frag = T.alloc_local((warp_rows * warp_cols * fragement_size_c), accum_dtype)
+
+            B_local = T.alloc_local([local_size_compressed], storage_dtype)
+            B_dequantize_local = T.alloc_local([local_size], in_dtype)
+
+            thread_bindings = T.thread_binding(0, threads, "threadIdx.x")
+
+            T.annotate_layout({
+                A_shared: make_swizzle_layout(A_shared),
+                B_dequantize_shared: make_swizzle_layout(B_dequantize_shared),
+            })
+
+            # Improve L2 Cache
+            T.use_swizzle(panel_size=10)
+
+            T.clear(C_frag)
+
+            for ko in T.Pipelined((K // block_K), num_stages=stage):
+
+                # Load A into shared memory
+                for i, k in T.Parallel(block_M, block_K):
+                    A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
+
+                # Load B into shared memory
+                for j, k in T.Parallel(block_N, block_K // num_elems_per_byte):
+                    B_shared[j, k] = B[bx * block_N + j, ko * (block_K // num_elems_per_byte) + k]
+
+                for i in T.serial(block_N * block_K // num_elems_per_byte //
+                                  (threads * local_size_compressed)):
+                    for v in T.vectorized(0, local_size_compressed):
+                        index = (
+                            i * threads * local_size_compressed +
+                            thread_bindings * local_size_compressed + v)
+                        vi, vj = T.index_to_coordinates(index, B_shared_shape)
+                        B_local[v] = B_shared[vi, vj]
+
+                    T.call_extern(
+                        "handle",
+                        "decode_i2u_to_i8s",
+                        T.address_of(B_local[0]),
+                        T.address_of(B_dequantize_local[0]),
+                    )
+
+                    for v in T.vectorized(0, local_size):
+                        index = (i * threads * local_size + thread_bindings * local_size + v)
+                        vi, vj = T.index_to_coordinates(index, B_dequantize_shared_shape)
+                        B_dequantize_shared[vi, vj] = B_dequantize_local[v]
+
+                for ki in T.serial(0, (block_K // micro_size_k)):
+
+                    # Load A into fragment
+                    mma_emitter.ldmatrix_a(
+                        A_frag,
+                        A_shared,
+                        ki,
+                    )
+
+                    # Load B into fragment
+                    mma_emitter.ldmatrix_b(
+                        B_frag,
+                        B_dequantize_shared,
+                        ki,
+                    )
+
+                    # Perform Matrix Multiplication
+                    mma_emitter.mma(A_frag, B_frag, C_frag)
+
+            # Perform STMatrix
+            mma_emitter.stmatrix(
+                C_frag,
+                C_shared,
+            )
+
+            # Store shared into global
+            for i, j in T.Parallel(block_M, block_N):
+                C[by * block_M + i, bx * block_N + j] = C_shared[
+                    i // micro_size_x,
+                    j // micro_size_y,
+                    i % micro_size_x,
+                    j % micro_size_y,
+                ]
+
+    return main
+
+
+def general_compress(lowprecision_weight, source_bits=4, storage_dtype=np.int8):
+    elems_per_byte = 8 // source_bits
+    if lowprecision_weight.dtype == np.float16:
+        lowprecision_weight = lowprecision_weight.astype(dtype=np.int8)
+    int8_weight = np.zeros(
+        (
+            *lowprecision_weight.shape[:-1],
+            lowprecision_weight.shape[-1] // elems_per_byte,
+        ),
+        dtype=np.int8,
+    )
+    for j in range(lowprecision_weight.shape[-1] // elems_per_byte):
+        for k in range(elems_per_byte):
+            int8_weight[:, j] |= lowprecision_weight[:, j * elems_per_byte + k] << (source_bits * k)
+
+    return int8_weight.view(storage_dtype)
+
+
+# interleave weight numpy implementation
+def interleave_weight(qweight, nbits=4, target_dtype="float16"):
+    assert target_dtype in ["float16", "int8"]
+    # reinterpret the data type of qweight to int32
+    qweight = qweight.view(np.int32)
+    new_qweight = np.zeros_like(qweight)
+    bits_stride = 8 if target_dtype == "int8" else 16
+    mask = (1 << nbits) - 1  # for 4bit the val is 0x0000000f
+    num_groups = 32 // bits_stride
+    elems_per_group = bits_stride // nbits
+    for i in range(num_groups):
+        for j in range(elems_per_group):
+            offset = i * elems_per_group + j
+            shift = (offset % num_groups) * bits_stride + (offset // num_groups) * nbits
+            new_qweight |= ((qweight >> (nbits * offset)) & mask) << shift
+
+    if nbits == 1 and target_dtype == "int8":
+        # special handling for 1b interleave
+        n16_weight = new_qweight & np.int32(0xF0F00F0F)
+        n16_weight |= ((new_qweight & np.int32(0x000000F0)) >> 4) << 16
+        n16_weight |= ((new_qweight & np.int32(0x0000F000)) >> 12) << 24
+        n16_weight |= ((new_qweight & np.int32(0x000F0000)) >> 16) << 4
+        n16_weight |= ((new_qweight & np.int32(0x0F000000)) >> 24) << 12
+        return n16_weight.view(np.int8)
+    elif nbits == 2 and target_dtype == "float16":
+        n8_weight = new_qweight & np.int32(0xFF0000FF)
+        n8_weight |= ((new_qweight & np.int32(0x0000FF00)) >> 8) << 16
+        n8_weight |= ((new_qweight & np.int32(0x00FF0000)) >> 16) << 8
+        return n8_weight.view(np.int8)
+    elif nbits == 1 and target_dtype == "float16":
+        n8_weight = new_qweight & 0xF000000F
+        n8_weight |= ((new_qweight & 0x000000F0) >> 4) << 8
+        n8_weight |= ((new_qweight & 0x00000F00) >> 8) << 16
+        n8_weight |= ((new_qweight & 0x0000F000) >> 12) << 24
+        n8_weight |= ((new_qweight & 0x000F0000) >> 16) << 4
+        n8_weight |= ((new_qweight & 0x00F00000) >> 20) << 12
+        n8_weight |= ((new_qweight & 0x0F000000) >> 24) << 20
+
+    return new_qweight.view(np.int8)
+
+
+def assert_bitnet_158_int8xint2_prefill_correctness(M,
+                                                    N,
+                                                    K,
+                                                    in_dtype,
+                                                    out_dtype,
+                                                    accum_dtype,
+                                                    fast_decoding=True):
+    program = bitnet_158_int8xint2_prefill(M, N, K, in_dtype, out_dtype, accum_dtype, fast_decoding)
+    print(program)
+    kernel = tilelang.compile(program)
+    src_code = kernel.get_kernel_source()
+    # src_code is the generated cuda source
+    assert src_code is not None
+    print(src_code)
+    A = torch.randint(0, 4, (M, K), device="cuda", dtype=getattr(torch, in_dtype))
+    B = torch.randint(0, 2, (N, K), device="cuda", dtype=getattr(torch, in_dtype))
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    qw = general_compress(B.cpu().numpy(), source_bits=2, storage_dtype=np.int8)
+    qw = interleave_weight(qw, 2, target_dtype=in_dtype)
+    qw = torch.from_numpy(qw).to(device="cuda")
+
+    kernel(A, qw, C)
+    # Get Reference Result
+    ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, accum_dtype))
+
+    print(ref_c)
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
+if __name__ == "__main__":
+    assert_bitnet_158_int8xint2_prefill_correctness(256, 256, 256, "int8", "int32", "int32")

examples/bitnet-1.58b/kernel_benchmark/tl_int8xint8.py

+import torch
+import torch.backends
+from bitblas import tvm as tvm
+from tvm import DataType
+from tvm import tl as TL
+import tvm.tl.language as T
+from bitblas.tl.utils import get_swizzle_layout
+from bitblas.tl.mma_macro_generator import (
+    TensorCoreIntrinEmitter,)
+from bitblas.base import simplify_prim_func
+
+torch.manual_seed(0)
+
+
+def make_swizzle_layout(shared_buf):
+    dtype = shared_buf.dtype
+    shape = shared_buf.shape
+
+    can_swizzle = shape[-1] * DataType(dtype).bits == 512
+    if not can_swizzle:
+        return T.Layout(shape, lambda *args: args)
+
+    def transform_func(i, j):
+        new_warp_i, new_warp_j = get_swizzle_layout(i, j, shape[-1], dtype)
+        return [new_warp_i, new_warp_j]
+
+    return T.Layout(shape, transform_func)
+
+
+@simplify_prim_func
+def tl_matmul(
+    M,
+    N,
+    K,
+    in_dtype,
+    out_dtype,
+    accum_dtype,
+):
+    assert in_dtype in [
+        "float16",
+        "int8",
+    ], "Currently only float16 and int8 are supported"
+    assert out_dtype in [
+        "float16",
+        "float32",
+        "int32",
+    ], "Currently only float16, float32 and int32 are supported"
+
+    micro_size_x = micro_size_y = micro_size_k = 16
+
+    if out_dtype == "int32":
+        micro_size_k = 32
+
+    # This is a debug config
+    block_row_warps = 2
+    block_col_warps = 2
+    warp_row_tiles = 64
+    warp_col_tiles = 64
+    chunk = 32 if in_dtype == "float16" else 64
+    shared_scope = "shared.dyn"
+
+    # Pipeline Stage
+    stage = 2
+
+    block_M = block_row_warps * warp_row_tiles
+    block_N = block_col_warps * warp_col_tiles
+    block_K = chunk
+
+    A_shape = (M, K)
+    B_shape = (N, K)
+    A_shared_shape = (block_M, block_K)
+    B_shared_shape = (block_N, block_K)
+    C_shared_shape = (
+        block_M // micro_size_x,
+        block_N // micro_size_y,
+        micro_size_x,
+        micro_size_y,
+    )
+
+    warp_size = 32
+    threads = warp_size * (block_row_warps * block_col_warps)
+    local_size_a = (micro_size_x * micro_size_k) // warp_size
+    local_size_b = (micro_size_y * micro_size_k) // warp_size
+    local_size_c = (micro_size_x * micro_size_y) // warp_size
+    warp_rows = warp_row_tiles // micro_size_x
+    warp_cols = warp_col_tiles // micro_size_y
+
+    # MMA Wrapper to Auto Generate Code for MMA
+    mma_emitter = TensorCoreIntrinEmitter(
+        a_dtype=in_dtype,
+        b_dtype=in_dtype,
+        accum_dtype=accum_dtype,
+        a_transposed=False,
+        b_transposed=True,
+        block_row_warps=block_row_warps,
+        block_col_warps=block_col_warps,
+        warp_row_tiles=warp_row_tiles,
+        warp_col_tiles=warp_col_tiles,
+        chunk=chunk,
+    )
+
+    @T.prim_func
+    def main(
+            A: T.Buffer(A_shape, in_dtype),
+            B: T.Buffer(B_shape, in_dtype),
+            C: T.Buffer((M, N), out_dtype),
+    ):
+        with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
+
+            A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
+            B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
+            C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
+            A_local = T.alloc_local((warp_rows * local_size_a), in_dtype)
+            B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
+            C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
+
+            thread_bindings = T.thread_binding(0, threads, "threadIdx.x")
+
+            T.annotate_layout({
+                A_shared: make_swizzle_layout(A_shared),
+                B_shared: make_swizzle_layout(B_shared),
+            })
+
+            # Improve L2 Cache
+            T.use_swizzle(panel_size=10)
+
+            T.clear(C_local)
+
+            for ko in T.Pipelined((K // block_K), num_stages=stage):
+
+                # Load A into shared memory
+                for i, k in T.Parallel(block_M, block_K):
+                    A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
+
+                # Load B into shared memory
+                for j, k in T.Parallel(block_N, block_K):
+                    B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
+
+                for ki in T.serial(0, (block_K // micro_size_k)):
+
+                    # Load A into fragment
+                    mma_emitter.ldmatrix_a(
+                        A_local,
+                        A_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                    )
+
+                    # Load B into fragment
+                    mma_emitter.ldmatrix_b(
+                        B_local,
+                        B_shared,
+                        ki,
+                        thread_bindings=thread_bindings,
+                    )
+
+                    # Perform Matrix Multiplication
+                    mma_emitter.mma(A_local, B_local, C_local)
+
+            # Perform STMatrix
+            mma_emitter.stmatrix(
+                C_local,
+                C_shared,
+                thread_bindings=thread_bindings,
+            )
+
+            # Store shared into global
+            for i, j in T.Parallel(block_M, block_N):
+                C[by * block_M + i, bx * block_N + j] = C_shared[
+                    i // micro_size_x,
+                    j // micro_size_y,
+                    i % micro_size_x,
+                    j % micro_size_y,
+                ]
+
+    return main
+
+
+def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype):
+    matmul = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype)
+    mod, params = TL.lower(matmul)
+    src_code = mod.imported_modules[0].get_source()
+    # src_code is the generated cuda source
+    assert src_code is not None
+    print(src_code)
+    if in_dtype == "int8":
+        A = torch.randint(-7, 7, (M, K), device="cuda", dtype=torch.int8)
+        B = torch.randint(-7, 7, (N, K), device="cuda", dtype=torch.int8)
+    else:
+        A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
+        B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
+
+    C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
+
+    mod = TL.Profiler(mod, params, [], TL.TensorSupplyType.Integer)
+
+    mod(A, B, C)
+
+    latency = mod.do_bench(mod.func, warmup=25)
+    print(f"Latency: {latency}")
+    # Ensure that the latency is not None
+    assert latency is not None
+
+    # Get Reference Result
+    ref_c = torch.matmul(A.to(torch.float32), B.T.to(torch.float32)).to(getattr(torch, accum_dtype))
+    print(C)
+    print(ref_c)
+    torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
+
+
+def test_assert_tl_matmul():
+    assert_tl_matmul_correctness(128, 128, 128, "float16", "float16", "float16")
+    assert_tl_matmul_correctness(128, 256, 256, "float16", "float32", "float32")
+
+
+if __name__ == "__main__":
+    # bitblas.testing.main()
+    # assert_tl_matmul_correctness(128, 128, 128, "float16", "float16", "float16")
+    # assert_tl_matmul_correctness(128, 128, 128, "int8", "int32", "int32")
+    assert_tl_matmul_correctness(16384, 16384, 16384, "int8", "int32", "int32")

examples/bitnet-1.58b/load_from_quantized.py

+import torch
+import bitblas
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+import os
+from transformers import GenerationConfig
+import time
+
+filepath = os.path.abspath(__file__)
+dirpath = os.path.dirname(filepath)
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+model_name_or_path = "BitBLASModel/open_llama_3b_1.58bits"
+saved_model_path = os.path.join(dirpath, "models", f"{model_name_or_path}_bitblas")
+
+
+def generate_text(model, tokenizer, prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.lm_head.weight.device)
+    # Generate cos and sin values
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    end_time = time.time()
+
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    generation_time = end_time - start_time
+    num_tokens = len(output_ids[0])
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_text
+
+
+def main():
+    # load quantized model
+    qmodel = BitnetForCausalLM.from_quantized(saved_model_path,).cuda().half()
+    tokenizer = BitnetTokenizer.from_pretrained(model_name_or_path, use_fast=False)
+    # print("original model generated text:")
+    # print(generate_text(model, tokenizer, "Hi, ", max_length=100))
+    input_ids = torch.ones((1, 1), dtype=torch.long).cuda()
+    # naive model inference
+    output = qmodel(input_ids)
+    print("original model output:", output)
+    print("quantized model generated text:")
+    print(generate_text(qmodel, tokenizer, "Hi, ", max_length=100))
+
+
+if __name__ == "__main__":
+    main()

examples/bitnet-1.58b/maint/README.md

+---
+license: mit
+---
+
+
+This is a BitBLAS Implementation for the reproduced 1.58bit model from [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B). We replaced the original simulated Int8x3bit Quantized Inference Kernel with BitBLAS INT8xINT2 Kernel. We also evaluated the model's correctness and performance through `eval_correctness.py` and `benchmark_inference_latency.py`.
+
+## Latest News
+
+- 08/09/2024 ✨: We provide a more efficient implementation for bitnet with vLLM, which should use special model checkpoints, to make the ckpt and study how to deploy, please checkout [Make Checkpoints for vLLM](#make-checkpoints-for-vllm).
+
+## Make Checkpoints for vLLM
+
+We provide two scripts to make the checkpoints for vLLM. The first script is `generate_bitnet_model_native_format.sh`, which is used to make a checkpoint with fp16 uncompressed metaadta, the main difference with the original checkpoint is the `quant_config.json`, which allow vLLM to load the model and execute with a quant extension.
+
+```bash
+# move to the integration directory
+cd /root/to/BitBLAS/integration/BitNet
+# make the checkpoint
+./maint/generate_bitnet_model_native_format.sh
+# the output ckpy will be saved in the `./models/ckpt_bitnet_b1_58-3B` directory
+```
+
+The second script is `generate_bitnet_model_bitblas_format.sh`, which is used to make a checkpoint with BitBLAS compressed metadata, which can avoid the online dequantize sage for the profiling of vLLM, which lead to more efficient memory utilization.
+
+```bash
+./maint/generate_bitnet_model_bitblas_format.sh ./models/ckpt_bitnet_b1_58-3B ./models/ckpt_bitnet_b1_58-3B_bitblas
+# the output ckpy will be saved in the `./models/ckpt_bitnet_b1_58-3B_bitblas` directory
+```
+
+Finnaly, you can use the ckpt in vLLM with:
+
+```bash
+cd vllm_workspace
+# inference with the ckpt with fp16 uncompressed metadata
+python3 inference_with_native_format.py
+# inference with the ckpt with BitBLAS compressed metadata
+python3 inference_with_bitblas_format.py
+```
+
+## BitBLAS Results
+
+### Performance
+
+**Note:** To reproduce the results of BitBLAS, Please checkout the `benchmark_inference_latency.py`. To reproduce the results of the original model, Please checkout the [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B) repo.
+
+|      Model      | Device | batchsize | in_seq |   model  | bitnet-1.58b-3b-huggingface | bitnet-1.58b-3b-bitblas |
+|:---------------:|:------:|:---------:|:------:|:--------:|:---------------------------:|:-----------------------:|
+| bitnet_b1_58-3B |  A100  |     1     |    1   | LLAMA-3B |         177.6729107         |       64.17962909       |
+| bitnet_b1_58-3B |  A100  |    128    |    1   | LLAMA-3B |         188.6145592         |       63.48158518       |
+| bitnet_b1_58-3B |  A100  |     1     |  2048  | LLAMA-3B |         348.7066031         |       202.6877999       |
+
+### On-the-Fly GPU Memory Footprint
+
+We measured the GPU memory footprint through the `nvidia-smi` command. Please checkout `nvidia_measure_memory.sh` to get the real-time GPU memory usage. And then start a `benchmark_model_10k_loops.py` workload to measure the overall GPU memory usage.
+
+|    **Model**    | **Device** | **batchsize** | **in_seq** | **bitnet-1.58b-3b-huggingface** | **bitnet-1.58b-3b-bitblas** |
+|:---------------:|:----------:|:-------------:|:----------:|:-------------------------------:|:---------------------------:|
+| bitnet_b1_58-3B |    A100    |       1       |      1     |             7595 MB             |           1729 MB           |
+| bitnet_b1_58-3B |    A100    |      128      |      1     |             7677 MB             |           1789 MB           |
+| bitnet_b1_58-3B |    A100    |       1       |    2048    |             8731 MB             |           3163 MB           |
+
+## PPL and Zero-shot Accuracy
+
+The number is Reported from the [1bitLLM/bitnet_b1_58-3B](https://huggingface.co/1bitLLM/bitnet_b1_58-3B), Please checkout the `eval_ppl.py`.
+
+PPL and zero-shot accuracy:
+| Models | PPL| ARCe| ARCc| HS | BQ | OQ | PQ | WGe | Avg
+|-------|-------|-------|-------|-------|-------|-------|-------|-------|-------|
+| FP16 700M (reported) | 12.33 | 54.7 | 23.0 | 37.0 | 60.0 | 20.2 | 68.9 | 54.8 | 45.5 |
+| BitNet b1.58 700M (reported) | 12.87 | 51.8 | 21.4 | 35.1 | 58.2 | 20.0 | 68.1 | 55.2 | 44.3 |
+| BitNet b1.58 700M (reproduced) | 12.78 | 51.4 | 21.8 | 35.0 | 59.6 | 20.6 | 67.5 | 55.4 | 44.5 |
+| FP16 1.3B (reported)    | 11.25  | 56.9 | 23.5 | 38.5 | 59.1 | 21.6 | 70.0 | 53.9 | 46.2
+| BitNet b1.58 1.3B (reported)    | 11.29  | 54.9 | 24.2 | 37.7 | 56.7 | 19.6 | 68.8 | 55.8 | 45.4 |
+| BitNet b1.58 1.3B (reproduced)    | 11.19 | 55.8 | 23.7 | 37.6 | 59.0 | 20.2 | 69.2 | 56.0 | 45.9
+| FP16 3B (reported)    | 10.04   | 62.1 | 25.6 | 43.3 | 61.8 | 24.6 | 72.1 | 58.2 | 49.7
+| BitNet b1.58 3B (reported)    | 9.91   | 61.4 | 28.3 | 42.9 | 61.5 | 26.6 | 71.5 | 59.3 | 50.2
+| BitNet b1.58 3B (reproduced)    | 9.88 | 60.9 | 28.0 | 42.3 | 58.3 | 26.0 | 71.4 | 60.3 | 49.6 |
+
+The differences between the reported numbers and the reproduced results are possibly variances from the training data processing, seeds, or other random factors.
+
+## Citations
+
+```bibtex
+@article{ma2024era,
+  title={The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits},
+  author={Ma, Shuming and Wang, Hongyu and Ma, Lingxiao and Wang, Lei and Wang, Wenhui and Huang, Shaohan and Dong, Li and Wang, Ruiping and Xue, Jilong and Wei, Furu},
+  journal={arXiv preprint arXiv:2402.17764},
+  year={2024}
+}
+```
\ No newline at end of file

examples/bitnet-1.58b/maint/create_bitblas_ckpt.py

+import argparse
+import torch
+import bitblas
+from transformers.utils.hub import cached_file
+import os
+from transformers import GenerationConfig
+import time
+import json
+
+import sys
+
+sys.path.insert(0, os.path.dirname(os.path.realpath(__file__)) + "/../")
+from modeling_bitnet import BitnetForCausalLM
+from tokenization_bitnet import BitnetTokenizer
+
+filepath = os.path.abspath(__file__)
+dirpath = os.path.dirname(filepath)
+
+torch.set_grad_enabled(False)
+bitblas.set_log_level("INFO")
+
+parser = argparse.ArgumentParser()
+parser.add_argument("--model_name_or_path", type=str, default="1bitLLM/bitnet_b1_58-3B")
+parser.add_argument("--saved_model_path", type=str, default=None)
+args = parser.parse_args()
+
+model_name_or_path = args.model_name_or_path
+saved_model_path = os.path.join(
+    dirpath, "models",
+    f"{model_name_or_path}_bitblas") if args.saved_model_path is None else args.saved_model_path
+
+
+def generate_text(model, tokenizer, prompt, max_length=100):
+    input_ids = tokenizer.encode(prompt, return_tensors="pt").to(model.lm_head.weight.device)
+    # Generate cos and sin values
+    seq_length = input_ids.size(1)
+    position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+    position_ids = position_ids.unsqueeze(0).expand_as(input_ids)
+
+    generation_config = GenerationConfig(
+        max_length=max_length,
+        do_sample=True,
+        top_k=50,
+        top_p=0.95,
+        num_return_sequences=1,
+    )
+
+    start_time = time.time()
+    output_ids = model.generate(input_ids, generation_config=generation_config)
+    end_time = time.time()
+
+    generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    generation_time = end_time - start_time
+    num_tokens = len(output_ids[0])
+    tokens_per_second = num_tokens / generation_time
+
+    print(f"Generated {num_tokens} tokens in {generation_time:.2f} seconds")
+    print(f"Tokens per second: {tokens_per_second:.2f}")
+
+    return generated_text
+
+
+def main():
+    model = (
+        BitnetForCausalLM.from_pretrained(
+            model_name_or_path,
+            use_flash_attention_2=False,
+            torch_dtype=torch.float16,
+        ).cuda().half())
+    tokenizer = BitnetTokenizer.from_pretrained(model_name_or_path, use_fast=False)
+
+    # print("original model generated text:")
+    # print(generate_text(model, tokenizer, "Hi, ", max_length=100))
+    input_ids = torch.ones((1, 1), dtype=torch.long).cuda()
+    # naive model inference
+    output = model(input_ids)
+    print("original model output:", output)
+
+    model.quantize(fuse_qkv=True, fuse_gateup=True)
+    print("original model generated text:")
+    print(generate_text(model, tokenizer, "Hi, ", max_length=100))
+
+    model.save_pretrained(saved_model_path)
+
+    # load quant config
+    quant_config_path = cached_file(model_name_or_path, "quantize_config.json")
+    with open(quant_config_path, "r") as f:
+        quant_config = json.load(f)
+    print("quant config:")
+    print(quant_config)
+    quant_config["checkpoint_format"] = "bitblas"
+    quant_config["fuse_qkv"] = True
+    quant_config["fuse_gateup"] = True
+
+    # save quant config
+    quant_config_path = os.path.join(saved_model_path, "quantize_config.json")
+    with open(quant_config_path, "w") as f:
+        json.dump(quant_config, f)
+    print("quant config saved to:", quant_config_path)
+
+    # copy benchmark filed into saved model path
+    file_list = [
+        "configuration_bitnet.py",
+        "eval_utils.py",
+        "modeling_bitnet.py",
+        "tokenization_bitnet.py",
+        "utils_quant.py",
+        "README.md",
+    ]
+    for file in file_list:
+        file_path = cached_file(model_name_or_path, file)
+        os.system(f"cp {file_path} {saved_model_path}")
+    # load quantized model
+    qmodel = BitnetForCausalLM.from_quantized(saved_model_path,).cuda().half()
+    print("quantized model generated text:")
+    print(generate_text(qmodel, tokenizer, "Hi, ", max_length=100))
+
+
+if __name__ == '__main__':
+    main()