upgrade to vllm 0.11.2 (#4400)
Bump vLLM version to v0.11.2 What's broken and changed by vLLM: 1. structured_output is broken by https://github.com/vllm-project/vllm/pull/26866 2. get_mrope_input_positions is broken by https://github.com/vllm-project/vllm/pull/28399 3. graph mode is broken by https://github.com/vllm-project/vllm/pull/25110 we'll upgrade torch to 2.8 to fix the problem later 4. embedding is broken by https://github.com/vllm-project/vllm/pull/27583 5. `get_attn_backend_cls` and attention backend is broken are broken by https://github.com/vllm-project/vllm/pull/28534 6. spec decode is broken by https://github.com/vllm-project/vllm/pull/28771 7. sp feature is broken by https://github.com/vllm-project/vllm/pull/27126 8. mtp is broken by https://github.com/vllm-project/vllm/pull/27922 9. lora is broken by https://github.com/vllm-project/vllm/pull/21068 10. execute_model is broken by https://github.com/vllm-project/vllm/pull/26866 11. `VLLM_DISABLE_SHARED_EXPERTS_STREAM` env is broken by https://github.com/vllm-project/vllm/pull/28159 12. kv cahe is broken by https://github.com/vllm-project/vllm/pull/27753 13. dp is broken by https://github.com/vllm-project/vllm/pull/25110 What's broken and changed by ourself: 1. qwen vl is broken by https://github.com/vllm-project/vllm/pull/28455 We'll remove model files in the future to avoid this kind of error 2. Engine core is broken by https://github.com/vllm-project/vllm/pull/23691 We'll remove the patch file in the future. 3. Ascend scheduler is broken by https://github.com/vllm-project/vllm/pull/28733 We'll remove ascend scheudler later. 4. qwen3-next is broken by https://github.com/vllm-project/vllm/pull/28083 We'll remove model files in the future to avoid this kind of error 5. qwen vl is broken by https://github.com/vllm-project/vllm/pull/27764. We'll remove model files in the future Known issue: 1. ray doesn't work 2. the accuracy of qwen3-next is not correct 3. qwen3-vl is broken 4. prefix cache+ ascend scheduler + deepseek v2 lite is broken. Co-authored-by: MengqingCao <cmq0113@163.com> Co-authored-by: hfadzxy <starmoon_zhang@163.com> Co-authored-by: leo-pony <nengjunma@outlook.com> Co-authored-by: 22dimensions <waitingwind@foxmail.com> Co-authored-by: shen-shanshan <467638484@qq.com> - vLLM version: v0.11.2 --------- Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com> Signed-off-by: MengqingCao <cmq0113@163.com> Signed-off-by: hfadzxy <starmoon_zhang@163.com> Signed-off-by: leo-pony <nengjunma@outlook.com> Co-authored-by: MengqingCao <cmq0113@163.com> Co-authored-by: hfadzxy <starmoon_zhang@163.com> Co-authored-by: leo-pony <nengjunma@outlook.com>
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wangxiyuan
@@ -169,132 +169,3 @@ def fused_recurrent_gated_delta_rule_fwd_kernel(
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p_ht = ht + (bos + i_t) * stride_final_state_token
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p_ht = p_ht + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
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tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
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@triton.heuristics({
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'USE_INITIAL_STATE':
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lambda args: args['h0'] is not None,
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'IS_VARLEN':
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lambda args: args['cu_seqlens'] is not None,
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"IS_CONTINUOUS_BATCHING":
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lambda args: args['ssm_state_indices'] is not None,
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"IS_SPEC_DECODING":
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lambda args: args['num_accepted_tokens'] is not None,
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})
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@triton.jit(do_not_specialize=['N', 'T'])
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def fused_recurrent_gated_delta_rule_fwd_kernel_0_11_0(
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q,
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k,
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v,
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g,
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beta,
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o,
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h0,
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ht,
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cu_seqlens,
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ssm_state_indices,
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num_accepted_tokens,
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scale,
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N: tl.constexpr, # num of sequences
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T: tl.constexpr, # num of tokens
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B: tl.constexpr,
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H: tl.constexpr,
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HV: tl.constexpr,
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K: tl.constexpr,
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V: tl.constexpr,
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BK: tl.constexpr,
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BV: tl.constexpr,
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stride_init_state_token: tl.constexpr,
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stride_final_state_token: tl.constexpr,
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stride_indices_seq: tl.constexpr,
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stride_indices_tok: tl.constexpr,
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USE_INITIAL_STATE: tl.constexpr, # whether to use initial state
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INPLACE_FINAL_STATE: tl.constexpr, # whether to store final state inplace
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IS_BETA_HEADWISE: tl.
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constexpr, # whether beta is headwise vector or scalar,
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USE_QK_L2NORM_IN_KERNEL: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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IS_CONTINUOUS_BATCHING: tl.constexpr,
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IS_SPEC_DECODING: tl.constexpr,
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):
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i_k, i_v, i_nh = tl.program_id(0), tl.program_id(1), tl.program_id(2)
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i_n, i_hv = i_nh // HV, i_nh % HV
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i_h = i_hv // (HV // H)
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if IS_VARLEN:
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bos, eos = tl.load(cu_seqlens + i_n).to(
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tl.int64), tl.load(cu_seqlens + i_n + 1).to(tl.int64)
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all = T
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T = eos - bos
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else:
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bos, eos = i_n * T, i_n * T + T
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all = B * T
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if T == 0:
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# no tokens to process for this sequence
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return
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o_k = i_k * BK + tl.arange(0, BK)
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o_v = i_v * BV + tl.arange(0, BV)
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mask_k = o_k < K
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mask_v = o_v < V
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mask_h = mask_k[:, None] & mask_v[None, :]
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b_h = tl.zeros([BK, BV], dtype=tl.float32)
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if USE_INITIAL_STATE:
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if IS_CONTINUOUS_BATCHING:
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if IS_SPEC_DECODING:
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i_t = tl.load(num_accepted_tokens + i_n).to(tl.int64) - 1
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else:
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i_t = 0
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p_h0 = h0 + tl.load(ssm_state_indices + i_n * stride_indices_seq +
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i_t).to(tl.int64) * stride_init_state_token
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else:
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p_h0 = h0 + bos * HV * K * V
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p_h0 = p_h0 + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
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b_h += tl.load(p_h0, mask=mask_h, other=0).to(tl.float32)
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for i_t in range(0, T):
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p_q = q + (bos * H + i_h) * K + o_k + H * K * i_t
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p_k = k + (bos * H + i_h) * K + o_k + H * K * i_t
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p_v = v + (bos * HV + i_hv) * V + o_v + HV * V * i_t
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if IS_BETA_HEADWISE:
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p_beta = beta + (bos * HV + i_hv) * V + o_v + HV * V * i_t
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else:
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p_beta = beta + bos * HV + i_hv + HV * i_t
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p_g = g + bos * HV + i_hv + HV * i_t
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p_o = o + ((i_k * all + bos) * HV + i_hv) * V + o_v + HV * V * i_t
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b_q = tl.load(p_q, mask=mask_k, other=0).to(tl.float32)
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b_k = tl.load(p_k, mask=mask_k, other=0).to(tl.float32)
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b_v = tl.load(p_v, mask=mask_v, other=0).to(tl.float32)
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b_g = tl.load(p_g).to(tl.float32)
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if USE_QK_L2NORM_IN_KERNEL:
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b_q = b_q / tl.sqrt(tl.sum(b_q * b_q) + 1e-6)
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b_k = b_k / tl.sqrt(tl.sum(b_k * b_k) + 1e-6)
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b_q = b_q * scale
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# [BK, BV]
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# b_h *= tl.exp(b_g)
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b_h *= exp(b_g)
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# [BV]
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b_v -= tl.sum(b_h * b_k[:, None], 0)
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if IS_BETA_HEADWISE:
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b_beta = tl.load(p_beta, mask=mask_v, other=0).to(tl.float32)
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else:
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b_beta = tl.load(p_beta).to(tl.float32)
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b_v *= b_beta
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# [BK, BV]
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b_h += b_k[:, None] * b_v[None, :]
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# [BV]
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b_o = tl.sum(b_h * b_q[:, None], 0)
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tl.store(p_o, b_o.to(p_o.dtype.element_ty), mask=mask_v)
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# keep the states for multi-query tokens
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if INPLACE_FINAL_STATE:
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p_ht = ht + tl.load(ssm_state_indices + i_n * stride_indices_seq +
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i_t).to(tl.int64) * stride_final_state_token
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else:
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p_ht = ht + (bos + i_t) * stride_final_state_token
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p_ht = p_ht + i_hv * K * V + o_k[:, None] * V + o_v[None, :]
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tl.store(p_ht, b_h.to(p_ht.dtype.element_ty), mask=mask_h)
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