Repository: hpcaitech/ColossalAI

Issue:
[tensor] fix some unittests

[tensor] fix some unittests

[tensor] fix some unittests



Discussion:

Resolving patch:
diff --git a/colossalai/kernel/triton/token_attention_kernel.py b/colossalai/kernel/triton/token_attention_kernel.py
new file mode 100644
--- /dev/null
+++ b/colossalai/kernel/triton/token_attention_kernel.py
@@ -0,0 +1,333 @@
+# Adapted from ModelTC https://github.com/ModelTC/lightllm
+
+import math
+
+import torch
+
+try:
+    import triton
+    import triton.language as tl
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+    print("please install triton from https://github.com/openai/triton")
+
+if HAS_TRITON:
+
+    @triton.jit
+    def _token_attn_1_kernel(Q, K, sm_scale, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len,
+                             attn_out, kv_cache_loc_b_stride, kv_cache_loc_s_stride, q_batch_stride, q_head_stride,
+                             q_head_dim_stride, k_batch_stride, k_head_stride, k_head_dim_stride, attn_head_stride,
+                             attn_batch_stride, HEAD_DIM: tl.constexpr, BLOCK_N: tl.constexpr):
+        current_batch = tl.program_id(0)
+        current_head = tl.program_id(1)
+        start_n = tl.program_id(2)
+
+        offs_d = tl.arange(0, HEAD_DIM)
+        current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
+        current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
+
+        current_batch_start_index = max_kv_cache_len - current_batch_seq_len
+        current_batch_end_index = max_kv_cache_len
+
+        off_q = current_batch * q_batch_stride + current_head * q_head_stride + offs_d * q_head_dim_stride
+
+        offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
+
+        block_stard_index = start_n * BLOCK_N
+        block_mask = tl.where(block_stard_index < current_batch_seq_len, 1, 0)
+
+        for start_mark in range(0, block_mask, 1):
+            q = tl.load(Q + off_q + start_mark)
+            offs_n_new = current_batch_start_index + offs_n
+            k_loc = tl.load(kv_cache_loc + kv_cache_loc_b_stride * current_batch + kv_cache_loc_s_stride * offs_n_new,
+                            mask=offs_n_new < current_batch_end_index,
+                            other=0)
+            off_k = k_loc[:, None] * k_batch_stride + current_head * k_head_stride + offs_d[None, :] * k_head_dim_stride
+            k = tl.load(K + off_k, mask=offs_n_new[:, None] < current_batch_end_index, other=0.0)
+            att_value = tl.sum(q[None, :] * k, 1)
+            att_value *= sm_scale
+            off_o = current_head * attn_head_stride + (current_batch_in_all_start_index + offs_n) * attn_batch_stride
+            tl.store(attn_out + off_o, att_value, mask=offs_n_new < current_batch_end_index)
+        return
+
+    @triton.jit
+    def _token_attn_1_alibi_kernel(Q, K, sm_scale, alibi, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen,
+                                   max_kv_cache_len, attn_out, kv_cache_loc_b_stride, kv_cache_loc_s_stride,
+                                   q_batch_stride, q_head_stride, q_head_dim_stride, k_batch_stride, k_head_stride,
+                                   k_head_dim_stride, attn_head_stride, attn_batch_stride, HEAD_DIM: tl.constexpr,
+                                   BLOCK_N: tl.constexpr):
+        current_batch = tl.program_id(0)
+        current_head = tl.program_id(1)
+        start_n = tl.program_id(2)
+
+        offs_d = tl.arange(0, HEAD_DIM)
+        current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
+        current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
+
+        current_batch_start_index = max_kv_cache_len - current_batch_seq_len
+        current_batch_end_index = max_kv_cache_len
+
+        off_q = current_batch * q_batch_stride + current_head * q_head_stride + offs_d * q_head_dim_stride
+
+        offs_n = start_n * BLOCK_N + tl.arange(0, BLOCK_N)
+
+        block_stard_index = start_n * BLOCK_N
+        block_mask = tl.where(block_stard_index < current_batch_seq_len, 1, 0)
+
+        for start_mark in range(0, block_mask, 1):
+            alibi_m = tl.load(alibi + current_head)
+            q = tl.load(Q + off_q + start_mark)
+            offs_n_new = current_batch_start_index + offs_n
+            k_loc = tl.load(kv_cache_loc + kv_cache_loc_b_stride * current_batch + kv_cache_loc_s_stride * offs_n_new,
+                            mask=offs_n_new < current_batch_end_index,
+                            other=0)
+            off_k = k_loc[:, None] * k_batch_stride + current_head * k_head_stride + offs_d[None, :] * k_head_dim_stride
+            k = tl.load(K + off_k, mask=offs_n_new[:, None] < current_batch_end_index, other=0.0)
+            att_value = tl.sum(q[None, :] * k, 1)
+            att_value *= sm_scale
+            att_value -= alibi_m * (current_batch_seq_len - 1 - offs_n)
+            off_o = current_head * attn_head_stride + (current_batch_in_all_start_index + offs_n) * attn_batch_stride
+            tl.store(attn_out + off_o, att_value, mask=offs_n_new < current_batch_end_index)
+        return
+
+    @torch.no_grad()
+    def token_attn_fwd_1(q,
+                         k,
+                         attn_out,
+                         kv_cache_loc,
+                         kv_cache_start_loc,
+                         kv_cache_seqlen,
+                         max_kv_cache_len,
+                         alibi=None):
+        BLOCK = 32
+        # shape constraints
+        q_head_dim, k_head_dim = q.shape[-1], k.shape[-1]
+        assert q_head_dim == k_head_dim
+        assert k_head_dim in {16, 32, 64, 128}
+        sm_scale = 1.0 / (k_head_dim**0.5)
+
+        batch, head_num = kv_cache_loc.shape[0], q.shape[1]
+
+        grid = (batch, head_num, triton.cdiv(max_kv_cache_len, BLOCK))
+
+        num_warps = 4 if k_head_dim <= 64 else 8
+        num_warps = 2
+
+        if alibi is not None:
+            _token_attn_1_alibi_kernel[grid](
+                q,
+                k,
+                sm_scale,
+                alibi,
+                kv_cache_loc,
+                kv_cache_start_loc,
+                kv_cache_seqlen,
+                max_kv_cache_len,
+                attn_out,
+                kv_cache_loc.stride(0),
+                kv_cache_loc.stride(1),
+                q.stride(0),
+                q.stride(1),
+                q.stride(2),
+                k.stride(0),
+                k.stride(1),
+                k.stride(2),
+                attn_out.stride(0),
+                attn_out.stride(1),
+                HEAD_DIM=k_head_dim,
+                BLOCK_N=BLOCK,
+                num_warps=num_warps,
+                num_stages=1,
+            )
+        else:
+            _token_attn_1_kernel[grid](
+                q,
+                k,
+                sm_scale,
+                kv_cache_loc,
+                kv_cache_start_loc,
+                kv_cache_seqlen,
+                max_kv_cache_len,
+                attn_out,
+                kv_cache_loc.stride(0),
+                kv_cache_loc.stride(1),
+                q.stride(0),
+                q.stride(1),
+                q.stride(2),
+                k.stride(0),
+                k.stride(1),
+                k.stride(2),
+                attn_out.stride(0),
+                attn_out.stride(1),
+                HEAD_DIM=k_head_dim,
+                BLOCK_N=BLOCK,
+                num_warps=num_warps,
+                num_stages=1,
+            )
+        return
+
+    @triton.jit
+    def _token_attn_softmax_fwd(softmax_logics, kv_cache_start_loc, kv_cache_seqlen, softmax_prob_out,
+                                logics_head_dim_stride, logics_batch_stride, prob_head_dim_stride, prob_batch_stride,
+                                BLOCK_SIZE: tl.constexpr):
+        current_batch = tl.program_id(0)
+        current_head = tl.program_id(1)
+
+        col_offsets = tl.arange(0, BLOCK_SIZE)
+        current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
+        current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
+
+        row = tl.load(softmax_logics + current_head * logics_head_dim_stride +
+                      (current_batch_in_all_start_index + col_offsets) * logics_batch_stride,
+                      mask=col_offsets < current_batch_seq_len,
+                      other=-float('inf')).to(tl.float32)
+
+        row_minus_max = row - tl.max(row, axis=0)
+        numerator = tl.exp(row_minus_max)
+        denominator = tl.sum(numerator, axis=0)
+        softmax_output = numerator / denominator
+
+        tl.store(softmax_prob_out + current_head * prob_head_dim_stride +
+                 (current_batch_in_all_start_index + col_offsets) * prob_batch_stride,
+                 softmax_output,
+                 mask=col_offsets < current_batch_seq_len)
+        return
+
+    @torch.no_grad()
+    def token_attn_softmax_fwd(softmax_logics, kv_cache_start_loc, kv_cache_seqlen, softmax_prob_out, max_kv_cache_len):
+        BLOCK_SIZE = triton.next_power_of_2(max_kv_cache_len)
+        batch, head_num = kv_cache_start_loc.shape[0], softmax_logics.shape[0]
+
+        num_warps = 4
+        if BLOCK_SIZE >= 2048:
+            num_warps = 8
+        if BLOCK_SIZE >= 4096:
+            num_warps = 16
+
+        _token_attn_softmax_fwd[(batch, head_num)](
+            softmax_logics,
+            kv_cache_start_loc,
+            kv_cache_seqlen,
+            softmax_prob_out,
+            softmax_logics.stride(0),
+            softmax_logics.stride(1),
+            softmax_prob_out.stride(0),
+            softmax_prob_out.stride(1),
+            num_warps=num_warps,
+            BLOCK_SIZE=BLOCK_SIZE,
+        )
+        return
+
+    @triton.jit
+    def _token_attn_2_kernel(Prob, V, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len,
+                             kv_cache_loc_b_stride, kv_cache_loc_s_stride, prob_head_dim_stride, prob_batch_stride,
+                             v_batch_stride, v_head_stride, v_head_dim_stride, attn_out_batch_stride,
+                             attn_out_head_stride, attn_out_head_dim_stride, HEAD_DIM: tl.constexpr,
+                             BLOCK_N: tl.constexpr):
+        current_batch = tl.program_id(0)
+        current_head = tl.program_id(1)
+
+        offs_n = tl.arange(0, BLOCK_N)
+        offs_d = tl.arange(0, HEAD_DIM)
+        current_batch_seq_len = tl.load(kv_cache_seqlen + current_batch)
+        current_batch_start_index = max_kv_cache_len - current_batch_seq_len
+        current_batch_end_index = current_batch_seq_len
+        current_batch_in_all_start_index = tl.load(kv_cache_start_loc + current_batch)
+
+        v_loc_off = current_batch * kv_cache_loc_b_stride + (current_batch_start_index + offs_n) * kv_cache_loc_s_stride
+        p_offs = current_head * prob_head_dim_stride + (current_batch_in_all_start_index + offs_n) * prob_batch_stride
+        v_offs = current_head * v_head_stride + offs_d[None, :] * v_head_dim_stride
+
+        acc = tl.zeros([HEAD_DIM], dtype=tl.float32)
+        for start_n in range(0, current_batch_seq_len, BLOCK_N):
+            start_n = tl.multiple_of(start_n, BLOCK_N)
+            p_value = tl.load(Prob + p_offs + start_n * kv_cache_loc_s_stride,
+                              mask=(start_n + offs_n) < current_batch_seq_len,
+                              other=0.0)
+            v_loc = tl.load(kv_cache_loc + v_loc_off + start_n * kv_cache_loc_s_stride,
+                            mask=(start_n + offs_n) < current_batch_seq_len,
+                            other=0.0)
+            v_value = tl.load(V + v_offs + v_loc[:, None] * v_batch_stride,
+                              mask=(start_n + offs_n[:, None]) < current_batch_seq_len,
+                              other=0.0)
+            acc += tl.sum(p_value[:, None] * v_value, 0)
+
+        acc = acc.to(tl.float16)
+        off_o = current_batch * attn_out_batch_stride + current_head * attn_out_head_stride + offs_d * attn_out_head_dim_stride
+        out_ptrs = attn_out + off_o
+        tl.store(out_ptrs, acc)
+        return
+
+    @torch.no_grad()
+    def token_attn_fwd_2(prob, v, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seqlen, max_kv_cache_len):
+        if triton.__version__ >= "2.1.0":
+            BLOCK = 128
+        else:
+            BLOCK = 64
+        batch, head = kv_cache_loc.shape[0], v.shape[1]
+        grid = (batch, head)
+        num_warps = 4
+        dim = v.shape[-1]
+
+        _token_attn_2_kernel[grid](
+            prob,
+            v,
+            attn_out,
+            kv_cache_loc,
+            kv_cache_start_loc,
+            kv_cache_seqlen,
+            max_kv_cache_len,
+            kv_cache_loc.stride(0),
+            kv_cache_loc.stride(1),
+            prob.stride(0),
+            prob.stride(1),
+            v.stride(0),
+            v.stride(1),
+            v.stride(2),
+            attn_out.stride(0),
+            attn_out.stride(1),
+            attn_out.stride(2),
+            HEAD_DIM=dim,
+            BLOCK_N=BLOCK,
+            num_warps=num_warps,
+            num_stages=1,
+        )
+        return
+
+    @torch.no_grad()
+    def token_attention_fwd(q,
+                            k,
+                            v,
+                            attn_out,
+                            kv_cache_loc,
+                            kv_cache_start_loc,
+                            kv_cache_seq_len,
+                            max_len_in_batch,
+                            alibi=None):
+        head_num = k.shape[1]
+        batch_size = kv_cache_seq_len.shape[0]
+        calcu_shape1 = (batch_size, head_num, k.shape[2])
+        total_token_num = k.shape[0]
+
+        att_m_tensor = torch.empty((head_num, total_token_num), dtype=q.dtype, device="cuda")
+
+        token_attn_fwd_1(q.view(calcu_shape1),
+                         k,
+                         att_m_tensor,
+                         kv_cache_loc,
+                         kv_cache_start_loc,
+                         kv_cache_seq_len,
+                         max_len_in_batch,
+                         alibi=alibi)
+
+        prob = torch.empty_like(att_m_tensor)
+
+        token_attn_softmax_fwd(att_m_tensor, kv_cache_start_loc, kv_cache_seq_len, prob, max_len_in_batch)
+        att_m_tensor = None
+        token_attn_fwd_2(prob, v, attn_out.view(calcu_shape1), kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len,
+                         max_len_in_batch)
+
+        prob = None
+
+        return


Test patch:
diff --git a/tests/test_kernels/triton/test_token_attn_1.py b/tests/test_kernels/triton/test_token_attn_1.py
new file mode 100644
--- /dev/null
+++ b/tests/test_kernels/triton/test_token_attn_1.py
@@ -0,0 +1,114 @@
+import math
+
+import pytest
+import torch
+from packaging import version
+
+try:
+    import triton
+    import triton.language as tl
+
+    from colossalai.kernel.triton.token_attention_kernel import token_attn_fwd_1
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+    print("please install triton from https://github.com/openai/triton")
+
+TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4')
+
+
+def torch_attn(xq, xk, bs, seqlen, num_head, head_dim):
+    xq = xq.view(bs, 1, num_head, head_dim)
+    xk = xk.view(bs, seqlen, num_head, head_dim)
+    keys = xk
+    xq = xq.transpose(1, 2)
+    keys = keys.transpose(1, 2)
+    scores = (torch.matmul(xq, keys.transpose(2, 3)) / math.sqrt(head_dim)).squeeze().transpose(0, 1).reshape(
+        num_head, -1)
+    return scores
+
+
+def torch_attn_1(xq, xk, seqlen, num_head, head_dim):
+    xq = xq.view(1, num_head, head_dim)
+    xk = xk.view(seqlen, num_head, head_dim)
+    logics = torch.sum(xq * xk, dim=-1, keepdim=False)
+
+    logics = logics.transpose(0, 1) / math.sqrt(head_dim)
+    return logics
+
+
+@pytest.mark.skipif(not TRITON_CUDA_SUPPORT or not HAS_TRITON,
+                    reason="triton requires cuda version to be higher than 11.4")
+def test_attn_1():
+    import time
+
+    batch_size, seq_len, head_num, head_dim = 17, 1025, 12, 128
+
+    dtype = torch.float16
+
+    q = torch.empty((batch_size, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2)
+    k = torch.empty((batch_size * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2)
+    attn_out = torch.empty((head_num, batch_size * seq_len), dtype=dtype, device="cuda")
+
+    b_loc = torch.zeros((batch_size, seq_len), dtype=torch.int32, device="cuda")
+    kv_cache_start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+    kv_cache_seq_len = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+
+    for i in range(batch_size):
+        kv_cache_start_loc[i] = i * seq_len
+        kv_cache_seq_len[i] = seq_len
+        b_loc[i] = i * seq_len + torch.arange(0, seq_len, dtype=torch.int32, device="cuda")
+
+    # Warm up
+    for _ in range(10):
+        token_attn_fwd_1(q, k, attn_out, b_loc, kv_cache_start_loc, kv_cache_seq_len, seq_len)
+    run_iter = 1000
+    torch.cuda.synchronize()
+    t1 = time.time()
+    for _ in range(run_iter):
+        token_attn_fwd_1(q, k, attn_out, b_loc, kv_cache_start_loc, kv_cache_seq_len, seq_len)
+    torch.cuda.synchronize()
+    t2 = time.time()
+    print("Time cost {}".format((t2 - t1) / run_iter))
+
+    torch_out = torch_attn(q, k, batch_size, seq_len, head_num, head_dim).squeeze()
+    o = attn_out.squeeze()
+    print("max ", torch.max(torch.abs(torch_out - o)))
+    print("mean ", torch.mean(torch.abs(torch_out - o)))
+    assert torch.allclose(torch_out, o, atol=1e-2, rtol=0)
+
+
+# def test_alibi_attn_1():
+#     import torch
+
+#     batch_size, seq_len, head_num, head_dim = 2, 1025, 12, 128
+
+#     dtype = torch.float16
+
+#     q = torch.empty((batch_size, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2)
+#     k = torch.empty((batch_size * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2)
+#     attn_out = torch.empty((head_num, batch_size * seq_len), dtype=dtype, device="cuda")
+
+#     # print(attn_out)
+
+#     b_loc = torch.zeros((batch_size, seq_len), dtype=torch.int32, device="cuda")
+#     kv_cache_start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+#     kv_cache_seq_len = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+
+#     for i in range(batch_size):
+#         kv_cache_start_loc[i] = i * seq_len
+#         kv_cache_seq_len[i] = seq_len
+#         b_loc[i] = i * seq_len + torch.arange(0, seq_len, dtype=torch.int32, device="cuda")
+#         # print(b_loc[i])
+
+#     token_attn_fwd_1(q, k, attn_out, b_loc, kv_cache_start_loc, kv_cache_seq_len, seq_len)
+
+#     torch_out = torch_attn(q, k, batch_size, seq_len, head_num, head_dim).squeeze()
+#     o = attn_out.squeeze()
+#     print("max ", torch.max(torch.abs(torch_out - o)))
+#     print("mean ", torch.mean(torch.abs(torch_out - o)))
+#     assert torch.allclose(torch_out, o, atol=1e-2, rtol=0)
+
+if __name__ == "__main__":
+    test_attn_1()
+    # test_alibi_attn_1()
diff --git a/tests/test_kernels/triton/test_token_attn_2.py b/tests/test_kernels/triton/test_token_attn_2.py
new file mode 100644
--- /dev/null
+++ b/tests/test_kernels/triton/test_token_attn_2.py
@@ -0,0 +1,70 @@
+import math
+
+import pytest
+import torch
+from packaging import version
+
+try:
+    import triton
+    import triton.language as tl
+
+    from colossalai.kernel.triton.token_attention_kernel import token_attn_fwd_2
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+    print("please install triton from https://github.com/openai/triton")
+
+TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4')
+
+
+def torch_attn(V, P, bs, seqlen, num_head, head_dim):
+    V = V.view(bs, seqlen, num_head, head_dim).transpose(1, 2)
+    P = P.reshape(num_head, bs, 1, seqlen).transpose(0, 1)
+    attn_out = torch.matmul(P, V)
+
+    return attn_out
+
+
+@pytest.mark.skipif(not TRITON_CUDA_SUPPORT or not HAS_TRITON,
+                    reason="triton requires cuda version to be higher than 11.4")
+def test_token_attn_2():
+    import time
+
+    batch_size, seq_len, head_num, head_dim = 17, 1025, 12, 128
+    dtype = torch.float16
+
+    V = torch.empty((batch_size * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=10)
+    Prob = torch.empty(
+        (head_num, batch_size * seq_len), dtype=dtype,
+        device="cuda").normal_(mean=0.4, std=0.2).reshape(head_num, batch_size,
+                                                          seq_len).softmax(-1).reshape(head_num, batch_size * seq_len)
+    attn_out = torch.empty((batch_size, head_num, head_dim), dtype=dtype, device="cuda")
+
+    kv_cache_start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+    kv_cache_seq_len = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+    kv_cache_loc = torch.zeros((batch_size, seq_len), dtype=torch.int32, device="cuda")
+    for i in range(batch_size):
+        kv_cache_start_loc[i] = i * seq_len
+        kv_cache_seq_len[i] = seq_len
+        kv_cache_loc[i] = i * seq_len + torch.arange(0, seq_len, dtype=torch.int32, device="cuda")
+
+    # Warm up
+    for _ in range(10):
+        token_attn_fwd_2(Prob, V, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, seq_len)
+    run_iter = 1000
+    torch.cuda.synchronize()
+    t1 = time.time()
+    for _ in range(run_iter):
+        token_attn_fwd_2(Prob, V, attn_out, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, seq_len)
+    torch.cuda.synchronize()
+    t2 = time.time()
+    print("Time cost {}".format((t2 - t1) / run_iter))
+    torch_out = torch_attn(V, Prob, batch_size, seq_len, head_num, head_dim).squeeze()
+    o = attn_out
+    print("max ", torch.max(torch.abs(torch_out - o)))
+    print("mean ", torch.mean(torch.abs(torch_out - o)))
+    assert torch.allclose(torch_out, o, atol=1e-2, rtol=0)
+
+
+if __name__ == "__main__":
+    test_token_attn_2()
diff --git a/tests/test_kernels/triton/test_token_attn_fwd.py b/tests/test_kernels/triton/test_token_attn_fwd.py
new file mode 100644
--- /dev/null
+++ b/tests/test_kernels/triton/test_token_attn_fwd.py
@@ -0,0 +1,78 @@
+import time
+
+import pytest
+import torch
+from packaging import version
+
+try:
+    import triton
+    import triton.language as tl
+
+    from colossalai.kernel.triton.token_attention_kernel import token_attention_fwd
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+    print("please install triton from https://github.com/openai/triton")
+
+TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4')
+
+
+def torch_att(xq, xk, xv, bs, seqlen, num_head, head_dim):
+    xq = xq.view(bs, 1, num_head, head_dim)
+    xk = xk.view(bs, seqlen, num_head, head_dim)
+    xv = xv.view(bs, seqlen, num_head, head_dim)
+
+    logics = torch.sum(xq * xk, dim=3, keepdim=False) * 1 / (head_dim**0.5)
+    prob = torch.softmax(logics, dim=1)
+    prob = prob.view(bs, seqlen, num_head, 1)
+
+    return torch.sum(prob * xv, dim=1, keepdim=False)
+
+
+@pytest.mark.skipif(not TRITON_CUDA_SUPPORT or not HAS_TRITON,
+                    reason="triton requires cuda version to be higher than 11.4")
+def test():
+
+    Z, head_num, seq_len, head_dim = 22, 112 // 8, 2048, 128
+    dtype = torch.float16
+    q = torch.empty((Z, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.1, std=0.2)
+    k = torch.empty((Z * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2)
+    v = torch.empty((Z * seq_len, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2)
+    o = torch.empty((Z, head_num, head_dim), dtype=dtype, device="cuda").normal_(mean=0.3, std=0.2)
+    alibi = torch.zeros((head_num,), dtype=torch.float32, device="cuda")
+
+    max_kv_cache_len = seq_len
+    kv_cache_start_loc = torch.zeros((Z,), dtype=torch.int32, device="cuda")
+    kv_cache_loc = torch.zeros((Z, seq_len), dtype=torch.int32, device="cuda")
+    kv_cache_seq_len = torch.ones((Z,), dtype=torch.int32, device="cuda")
+
+    kv_cache_seq_len[:] = seq_len
+    kv_cache_start_loc[0] = 0
+    kv_cache_start_loc[1] = seq_len
+    kv_cache_start_loc[2] = 2 * seq_len
+    kv_cache_start_loc[3] = 3 * seq_len
+
+    for i in range(Z):
+        kv_cache_loc[i, :] = torch.arange(i * seq_len, (i + 1) * seq_len, dtype=torch.int32, device="cuda")
+
+    token_attention_fwd(q, k, v, o, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_kv_cache_len, alibi=alibi)
+    torch.cuda.synchronize()
+    start = time.time()
+    token_attention_fwd(q, k, v, o, kv_cache_loc, kv_cache_start_loc, kv_cache_seq_len, max_kv_cache_len, alibi=alibi)
+    torch.cuda.synchronize()
+    print("cost time:", (time.time() - start) * 1000)
+
+    torch_att(q, k, v, Z, seq_len, head_num, head_dim)
+    torch.cuda.synchronize()
+    start = time.time()
+    torch_out = torch_att(q, k, v, Z, seq_len, head_num, head_dim)
+    torch.cuda.synchronize()
+    print("cost time:", (time.time() - start) * 1000)
+
+    print("max ", torch.max(torch.abs(torch_out - o)))
+    print("mean ", torch.mean(torch.abs(torch_out - o)))
+    assert torch.allclose(torch_out, o, atol=1e-2, rtol=0)
+
+
+if __name__ == "__main__":
+    test()
diff --git a/tests/test_kernels/triton/test_token_softmax.py b/tests/test_kernels/triton/test_token_softmax.py
new file mode 100644
--- /dev/null
+++ b/tests/test_kernels/triton/test_token_softmax.py
@@ -0,0 +1,48 @@
+import pytest
+import torch
+from packaging import version
+
+try:
+    import triton
+    import triton.language as tl
+
+    from colossalai.kernel.triton.token_attention_kernel import token_attn_softmax_fwd
+    HAS_TRITON = True
+except ImportError:
+    HAS_TRITON = False
+    print("please install triton from https://github.com/openai/triton")
+
+TRITON_CUDA_SUPPORT = version.parse(torch.version.cuda) > version.parse('11.4')
+
+
+@pytest.mark.skipif(not TRITON_CUDA_SUPPORT or not HAS_TRITON,
+                    reason="triton requires cuda version to be higher than 11.4")
+def test_softmax():
+
+    import torch
+
+    batch_size, seq_len, head_num, head_dim = 4, 1025, 12, 128
+
+    dtype = torch.float16
+
+    Logics = torch.empty((head_num, batch_size * seq_len), dtype=dtype, device="cuda").normal_(mean=0.1, std=10)
+    ProbOut = torch.empty((head_num, batch_size * seq_len), dtype=dtype, device="cuda").normal_(mean=0.4, std=0.2)
+
+    kv_cache_start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+    kv_cache_seq_len = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+
+    for i in range(batch_size):
+        kv_cache_start_loc[i] = i * seq_len
+        kv_cache_seq_len[i] = seq_len
+
+    token_attn_softmax_fwd(Logics, kv_cache_start_loc, kv_cache_seq_len, ProbOut, seq_len)
+
+    torch_out = Logics.reshape(head_num * batch_size, -1).softmax(-1).reshape(head_num, batch_size * seq_len)
+    o = ProbOut
+    print("max ", torch.max(torch.abs(torch_out - o)))
+    print("mean ", torch.mean(torch.abs(torch_out - o)))
+    assert torch.allclose(torch_out, o, atol=1e-2, rtol=0)
+
+
+if __name__ == "__main__":
+    test_softmax()