[BugFix][main] Adapted Qwen3-Next-MTP to chunked prefill (#4770)
### What this PR does / why we need it?
The pad `-1` modification is from
https://github.com/vllm-project/vllm/pull/25743.
It still has bugs for batched chunked prefill.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
Signed-off-by: drslark <slarksblood@qq.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
This commit is contained in:
drslark
@@ -132,6 +132,490 @@ def causal_conv1d_fn(
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return out_ref_tensor
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# TODO copied from vllm and it needs to be optimized
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@triton.jit()
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def _original_causal_conv1d_update_kernel(
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# Pointers to matrices
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x_ptr, # (batch, dim, seqlen)
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w_ptr, # (dim, width)
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bias_ptr,
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conv_state_ptr,
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conv_state_indices_ptr,
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num_accepted_tokens_ptr,
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query_start_loc_ptr, # (batch + 1)
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block_idx_last_scheduled_token, # (batch,)
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initial_state_idx, # (batch,)
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o_ptr, # (batch, dim, seqlen)
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# Matrix dimensions
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batch: int,
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dim: tl.constexpr,
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seqlen: tl.constexpr,
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state_len: tl.constexpr,
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num_cache_lines: tl.constexpr, # added to support vLLM larger cache lines
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# Strides
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stride_x_seq: tl.constexpr,
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stride_x_dim: tl.constexpr,
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stride_x_token: tl.constexpr,
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stride_w_dim: tl.constexpr,
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stride_w_width: tl.constexpr,
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stride_conv_state_seq: tl.constexpr,
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stride_conv_state_dim: tl.constexpr,
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stride_conv_state_tok: tl.constexpr,
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stride_state_indices: tl.constexpr,
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stride_o_seq: tl.constexpr,
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stride_o_dim: tl.constexpr,
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stride_o_token: tl.constexpr,
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# others
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pad_slot_id: tl.constexpr,
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# Meta-parameters
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HAS_BIAS: tl.constexpr,
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KERNEL_WIDTH: tl.constexpr,
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SILU_ACTIVATION: tl.constexpr,
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IS_VARLEN: tl.constexpr,
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IS_APC_ENABLED: tl.constexpr,
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IS_SPEC_DECODING: tl.constexpr,
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NP2_STATELEN: tl.constexpr,
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USE_PAD_SLOT: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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# ruff: noqa: E501
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idx_seq = tl.program_id(0)
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if idx_seq >= batch:
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return
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# [BLOCK_N,] elements along the feature-dimension (channel)
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idx_feats = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
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if IS_APC_ENABLED:
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# Get the state from the initial_state_idx
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conv_state_init = tl.load(initial_state_idx + idx_seq)
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current_last_index = tl.load(block_idx_last_scheduled_token + idx_seq)
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else:
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conv_state_init = 0
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current_last_index = 0
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# cache_idx
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conv_states_input_coord = tl.load(conv_state_indices_ptr +
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idx_seq * stride_state_indices +
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conv_state_init).to(tl.int64)
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if USE_PAD_SLOT: # noqa
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if conv_states_input_coord == pad_slot_id:
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# not processing as this is not the actual sequence
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return
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if IS_VARLEN:
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query_start_index = tl.load(query_start_loc_ptr + idx_seq).to(tl.int64)
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query_end_index = tl.load(query_start_loc_ptr + (idx_seq + 1)).to(
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tl.int64)
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# revise state_len and seqlen
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state_len = state_len - (seqlen -
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(query_end_index - query_start_index))
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seqlen = query_end_index - query_start_index
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x_offset = query_start_index * stride_x_token
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o_offset = query_start_index * stride_o_token
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else:
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query_start_index = idx_seq * seqlen
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query_end_index = query_start_index + seqlen
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x_offset = idx_seq * stride_x_seq
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o_offset = idx_seq * stride_o_seq
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if query_start_index == query_end_index:
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return
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if IS_SPEC_DECODING:
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# The rolling of conv state:
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#
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# Before forward, the conv_state is:
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# [history1, history2, ..., historyM].
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#
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# After forward, the conv_state becomes:
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# [history2, ..., historyM, draft1, draft2, ..., draftN].
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#
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# After acceptance, it becomes:
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#
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# - accept 1 tokens: [history2, ..., historyM, draft1]
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# - accept 2 tokens: [history3, ..., historyM, draft1, draft2]
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# - and so on.
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conv_state_token_offset = (
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tl.load(num_accepted_tokens_ptr + idx_seq).to(tl.int64) - 1)
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else:
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conv_state_token_offset = 0
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# STEP 1: READ init_state data
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conv_states_base = (conv_state_ptr +
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(conv_states_input_coord * stride_conv_state_seq) +
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(idx_feats * stride_conv_state_dim))
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mask_w = idx_feats < dim
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prior_tokens = conv_states_base + conv_state_token_offset * stride_conv_state_tok
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if KERNEL_WIDTH >= 2:
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conv_states_ptrs = prior_tokens # [BLOCK_N]
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col0 = tl.load(conv_states_ptrs, mask_w, 0.0)
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if KERNEL_WIDTH >= 3:
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conv_states_ptrs = prior_tokens + 1 * stride_conv_state_tok # [BLOCK_N]
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col1 = tl.load(conv_states_ptrs, mask_w, 0.0)
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if KERNEL_WIDTH >= 4:
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conv_states_ptrs = prior_tokens + 2 * stride_conv_state_tok # [BLOCK_N]
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col2 = tl.load(conv_states_ptrs, mask_w, 0.0)
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if KERNEL_WIDTH >= 5:
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conv_states_ptrs = prior_tokens + 3 * stride_conv_state_tok # [BLOCK_N]
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col3 = tl.load(conv_states_ptrs, mask_w, 0.0)
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if KERNEL_WIDTH >= 6:
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conv_states_ptrs = prior_tokens + 4 * stride_conv_state_tok # [BLOCK_N]
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col4 = tl.load(conv_states_ptrs, mask_w, 0.0)
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# STEP 2: assume state_len > seqlen
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idx_tokens = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
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# With speculative decoding, the conv_state updates works in a sliding
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# window manner, at each forward pass, the tokens are shift by 1, so we
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# load since idx_tokens + 1.
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conv_state_ptrs_source = (
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conv_state_ptr + (conv_states_input_coord * stride_conv_state_seq) +
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conv_state_token_offset * stride_conv_state_tok +
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(idx_feats * stride_conv_state_dim)[None, :] +
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((idx_tokens + (1 if IS_SPEC_DECODING else seqlen)) *
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stride_conv_state_tok)[:, None]) # [BLOCK_M, BLOCK_N]
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mask = ((conv_states_input_coord < num_cache_lines)
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& ((idx_tokens + seqlen) < state_len)[:, None]
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& (idx_feats < dim)[None, :])
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conv_state = tl.load(conv_state_ptrs_source, mask, other=0.0)
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VAL = state_len - seqlen
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x_base = x_ptr + x_offset + (idx_feats * stride_x_dim) # [BLOCK_N]
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x_ptrs = (x_base[None, :] + ((idx_tokens - VAL) * stride_x_token)[:, None]
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) # [BLOCK_M, BLOCK_N]
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mask_x = ((idx_tokens - VAL >= 0)[:, None]
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& (idx_tokens - VAL < seqlen)[:, None]
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& (idx_feats < dim)[None, :]
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) # token-index # token-index # feature-index
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loaded_x = tl.load(x_ptrs, mask_x, 0.0)
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tl.debug_barrier()
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new_conv_state = tl.where(mask, conv_state, loaded_x)
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# Get the state from the initial_state_idx
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# cache_idx
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conv_states_offset = tl.load(conv_state_indices_ptr +
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idx_seq * stride_state_indices +
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current_last_index).to(tl.int64)
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conv_state_ptrs_target = (
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conv_state_ptr +
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(conv_states_offset * stride_conv_state_seq) # Offset from seq
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+ (idx_feats * stride_conv_state_dim))[None, :] + ( # [BLOCK_N,]
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idx_tokens * stride_conv_state_tok)[:, None]
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mask = (idx_tokens < state_len)[:, None] & (idx_feats < dim)[None, :]
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tl.store(conv_state_ptrs_target, new_conv_state, mask)
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# STEP 3: init accumulator
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if HAS_BIAS:
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bias = bias_ptr + idx_feats
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mask_bias = idx_feats < dim
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acc_preload = tl.load(bias, mask=mask_bias,
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other=0.0).to(tl.float32) # [BLOCK_N]
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else:
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acc_preload = tl.zeros((BLOCK_N, ), dtype=tl.float32)
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# STEP 4:
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# PRE-LOAD WEIGHTS
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# first kernel column, configured for weights to handle BLOCK_N features in range
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w_base = w_ptr + (idx_feats * stride_w_dim) # [BLOCK_N,]
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mask_w = idx_feats < dim
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if KERNEL_WIDTH >= 2:
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w_ptrs = w_base + (0 * stride_w_width) # [BLOCK_N] tensor
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w_col0 = tl.load(w_ptrs, mask_w, other=0.0)
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w_ptrs = w_base + (1 * stride_w_width) # [BLOCK_N] tensor
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w_col1 = tl.load(w_ptrs, mask_w, other=0.0)
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if KERNEL_WIDTH >= 3:
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w_ptrs = w_base + (2 * stride_w_width) # [BLOCK_N] tensor
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w_col2 = tl.load(w_ptrs, mask_w, other=0.0)
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if KERNEL_WIDTH >= 4:
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w_ptrs = w_base + (3 * stride_w_width) # [BLOCK_N] tensor
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w_col3 = tl.load(w_ptrs, mask_w, other=0.0)
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if KERNEL_WIDTH >= 5:
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w_ptrs = w_base + (4 * stride_w_width) # [BLOCK_N] tensor
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w_col4 = tl.load(w_ptrs, mask_w, other=0.0)
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if KERNEL_WIDTH >= 6:
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w_ptrs = w_base + (5 * stride_w_width) # [BLOCK_N] tensor
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w_col5 = tl.load(w_ptrs, mask_w, other=0.0)
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x_base_1d = x_base # starting of chunk [BLOCK_N]
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mask_x_1d = idx_feats < dim
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# STEP 5: compute each token
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for idx_token in tl.range(seqlen):
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acc = acc_preload
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matrix_w = w_col0
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matrix_x = col0
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for j in tl.static_range(KERNEL_WIDTH):
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if KERNEL_WIDTH == 2:
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if j == 1: # KERNEL_WIDTH-1:
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matrix_w = w_col1
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x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
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matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
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elif KERNEL_WIDTH == 3:
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if j == 1:
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matrix_w = w_col1
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matrix_x = col1
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elif j == 2:
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matrix_w = w_col2
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x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
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matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
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elif KERNEL_WIDTH == 4:
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if j == 1:
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matrix_w = w_col1
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matrix_x = col1
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elif j == 2:
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matrix_w = w_col2
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matrix_x = col2
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elif j == 3:
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matrix_w = w_col3
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x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
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matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
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elif KERNEL_WIDTH == 5:
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if j == 1:
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matrix_w = w_col1
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matrix_x = col1
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elif j == 2:
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matrix_w = w_col2
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matrix_x = col2
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elif j == 3:
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matrix_w = w_col3
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matrix_x = col3
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elif j == 4:
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matrix_w = w_col4
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x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
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matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
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elif KERNEL_WIDTH == 6:
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if j == 1:
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matrix_w = w_col1
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matrix_x = col1
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elif j == 2:
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matrix_w = w_col2
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matrix_x = col2
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elif j == 3:
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matrix_w = w_col3
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matrix_x = col3
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elif j == 4:
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matrix_w = w_col4
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matrix_x = col4
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elif j == 5:
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matrix_w = w_col5
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x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
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matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
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acc += matrix_x * matrix_w # [BLOCK_N]
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if KERNEL_WIDTH == 2:
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col0 = matrix_x
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elif KERNEL_WIDTH == 3:
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col0 = col1
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col1 = matrix_x
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elif KERNEL_WIDTH == 4:
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col0 = col1
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col1 = col2
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col2 = matrix_x
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elif KERNEL_WIDTH == 5:
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col0 = col1
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col1 = col2
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col2 = col3
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col3 = matrix_x
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elif KERNEL_WIDTH == 6:
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col0 = col1
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col1 = col2
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col2 = col3
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col3 = col4
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col4 = matrix_x
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if SILU_ACTIVATION:
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acc = acc / (1 + tl.exp(-acc))
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mask_1d = (idx_token < seqlen) & (idx_feats < dim
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) # token-index # feature-index
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o_ptrs = (o_ptr + o_offset + idx_token * stride_o_token +
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(idx_feats * stride_o_dim))
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tl.store(o_ptrs, acc, mask=mask_1d)
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# TODO copied from vllm and it needs to be optimized
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def original_causal_conv1d_update(
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x: torch.Tensor,
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conv_state: torch.Tensor,
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weight: torch.Tensor,
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bias: torch.Tensor | None = None,
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activation: bool | str | None = None,
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conv_state_indices: torch.Tensor | None = None,
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num_accepted_tokens: torch.Tensor | None = None,
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query_start_loc: torch.Tensor | None = None,
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max_query_len: int = -1,
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pad_slot_id: int = PAD_SLOT_ID,
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block_idx_last_scheduled_token: torch.Tensor | None = None,
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initial_state_idx: torch.Tensor | None = None,
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validate_data=False,
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):
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"""
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x: Input tensor which can take the following shapes:
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- `[batch, dim]` - single token prediction
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- `[batch, dim, seqlen]` - single or multiple tokens prediction
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- `[num_tokens, dim]` - continuous batching, where num_tokens is
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the total tokens of all sequences in that batch
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conv_state: (..., dim, state_len), where state_len >= width - 1
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weight: (dim, width)
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bias: (dim,)
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conv_state_indices: (batch,), dtype int32
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If not None, the conv_state is a larger tensor along the batch dim,
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and we are selecting the batch coords specified by conv_state_indices.
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Useful for a continuous batching scenario.
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block_idx_last_scheduled_token: (batch,), dtype int32
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The pointer into conv_state_indices, where the last cache block to be filled is located.
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initial_state_idx: (batch,), dtype int32
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The pointer into conv_state_indices, where the cache block containing the initial state is located.
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num_accepted_tokens: (batch,), dtype int32
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If not None, it indicates the number of accepted tokens for each
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sequence in the batch.
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This is used in speculative decoding, where the conv_state is updated
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in a sliding window manner.
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query_start_loc: (batch + 1,) int32
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If not None, the inputs is given in a varlen fashion and this indicates
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the starting index of each sequence in the batch.
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max_query_len: int
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If query_start_loc is not None, this indicates the maximum query
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length in the batch.
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pad_slot_id: int
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if conv_state_indices is passed, lets the kernel identify padded
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entries that will not be processed,
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for example: conv_state_indices = [pad_slot_id, 1 ,20 ,pad_slot_id]
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in this case, the kernel will not process entries at
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indices 0 and 3
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out: (batch, dim) or (batch, dim, seqlen) or (num_tokens, dim), same shape as `x`
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"""
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if validate_data:
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assert pad_slot_id is not None
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assert x.stride(1) == 1
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if isinstance(activation, bool):
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activation = "silu" if activation is True else None
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elif activation is not None:
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assert activation in ["silu", "swish"]
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original_x_dtype = x.dtype
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x = x.to(conv_state.dtype)
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unsqueeze = query_start_loc is None and x.dim() == 2
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if unsqueeze:
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# make it (batch, dim, seqlen) with seqlen == 1
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x = x.unsqueeze(-1)
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if query_start_loc is None:
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batch, dim, seqlen = x.shape
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else:
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assert conv_state_indices is not None
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batch = conv_state_indices.size(0)
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dim = x.size(1)
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seqlen = max_query_len
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_, width = weight.shape
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# conv_state: (..., dim, state_len), where state_len >= width - 1
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num_cache_lines, _, state_len = conv_state.size()
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if validate_data:
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assert dim == weight.size(0)
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assert conv_state.stride(-2) == 1, (
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f"ERROR: expect contiguous along feat-dim of conv_state (currently stride={conv_state.stride()})"
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)
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assert state_len >= width - 1
|
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# when above happens, we don't shift-left to keep any records in conv_state
|
||||
assert dim == conv_state.size(1)
|
||||
if conv_state_indices is None:
|
||||
assert conv_state.size(0) >= batch
|
||||
else:
|
||||
assert (batch, ) == conv_state_indices.shape
|
||||
|
||||
assert num_cache_lines >= batch
|
||||
assert weight.stride(1) == 1 # Need this
|
||||
|
||||
# adopt the strategy in vLLM that overwrite on 'x' directly, rather than creating a new tensor 'o'
|
||||
out = x
|
||||
stride_w_dim, stride_w_width = weight.stride()
|
||||
|
||||
if query_start_loc is None:
|
||||
# X (batch, dim, seqlen)
|
||||
stride_x_seq, stride_x_dim, stride_x_token = x.stride()
|
||||
stride_o_seq, stride_o_dim, stride_o_token = out.stride()
|
||||
else:
|
||||
# X (dim, cu_seqlen)
|
||||
stride_x_token, stride_x_dim = x.stride()
|
||||
stride_x_seq = 0
|
||||
stride_o_token, stride_o_dim = out.stride()
|
||||
stride_o_seq = 0
|
||||
|
||||
stride_istate_seq, stride_istate_dim, stride_istate_token = conv_state.stride(
|
||||
)
|
||||
stride_state_indices = (conv_state_indices.stride(0)
|
||||
if conv_state_indices is not None else 0)
|
||||
if num_accepted_tokens is not None:
|
||||
state_len = width - 1 + (seqlen - 1) # effective state_len needed
|
||||
else:
|
||||
state_len = width - 1
|
||||
np2_statelen = triton.next_power_of_2(state_len)
|
||||
|
||||
def grid(META):
|
||||
return (
|
||||
batch,
|
||||
triton.cdiv(dim, META["BLOCK_N"]),
|
||||
)
|
||||
|
||||
_original_causal_conv1d_update_kernel[grid](
|
||||
# Pointers to matrices
|
||||
x,
|
||||
weight,
|
||||
bias,
|
||||
conv_state,
|
||||
conv_state_indices,
|
||||
num_accepted_tokens,
|
||||
query_start_loc,
|
||||
block_idx_last_scheduled_token,
|
||||
initial_state_idx,
|
||||
out,
|
||||
# Matrix dimensions
|
||||
batch,
|
||||
dim,
|
||||
seqlen,
|
||||
state_len,
|
||||
num_cache_lines,
|
||||
# stride
|
||||
stride_x_seq,
|
||||
stride_x_dim,
|
||||
stride_x_token,
|
||||
stride_w_dim,
|
||||
stride_w_width,
|
||||
stride_istate_seq,
|
||||
stride_istate_dim,
|
||||
stride_istate_token,
|
||||
stride_state_indices,
|
||||
stride_o_seq,
|
||||
stride_o_dim,
|
||||
stride_o_token,
|
||||
# others
|
||||
pad_slot_id,
|
||||
# META
|
||||
HAS_BIAS=bias is not None,
|
||||
KERNEL_WIDTH=width,
|
||||
SILU_ACTIVATION=activation in ["silu", "swish"],
|
||||
IS_VARLEN=query_start_loc is not None,
|
||||
IS_APC_ENABLED=block_idx_last_scheduled_token is not None,
|
||||
IS_SPEC_DECODING=num_accepted_tokens is not None,
|
||||
NP2_STATELEN=np2_statelen,
|
||||
USE_PAD_SLOT=pad_slot_id is not None,
|
||||
BLOCK_N=256,
|
||||
)
|
||||
if unsqueeze:
|
||||
out = out.squeeze(-1)
|
||||
return out.to(original_x_dtype)
|
||||
|
||||
|
||||
@triton.jit()
|
||||
def _causal_conv1d_update_kernel(
|
||||
# Pointers to matrices
|
||||
@@ -392,6 +876,8 @@ def causal_conv1d_update_npu(
|
||||
cache_seqlens: Optional[torch.Tensor] = None,
|
||||
conv_state_indices: Optional[torch.Tensor] = None,
|
||||
num_accepted_tokens: Optional[torch.Tensor] = None,
|
||||
query_start_loc: torch.Tensor | None = None,
|
||||
max_query_len: int = -1,
|
||||
intermediate_conv_window: Optional[torch.Tensor] = None,
|
||||
pad_slot_id: int = PAD_SLOT_ID,
|
||||
metadata=None,
|
||||
@@ -421,6 +907,19 @@ def causal_conv1d_update_npu(
|
||||
indices 0 and 3
|
||||
out: (batch, dim) or (batch, dim, seqlen)
|
||||
"""
|
||||
if query_start_loc is not None:
|
||||
return original_causal_conv1d_update(
|
||||
x=x,
|
||||
conv_state=conv_state,
|
||||
weight=weight,
|
||||
bias=bias,
|
||||
activation=activation,
|
||||
conv_state_indices=conv_state_indices,
|
||||
num_accepted_tokens=num_accepted_tokens,
|
||||
query_start_loc=query_start_loc,
|
||||
max_query_len=max_query_len,
|
||||
validate_data=validate_data)
|
||||
|
||||
if validate_data:
|
||||
assert cache_seqlens is None # not implemented yet - ok for vLLM
|
||||
assert pad_slot_id is not None
|
||||
|
||||
Reference in New Issue
Block a user