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540 lines
20 KiB
Python
540 lines
20 KiB
Python
# adapted from vllm/model_executor/layers/mamba/ops/casual_conv1d.py
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# Adapted from https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
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# SPDX-License-Identifier: Apache-2.0
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# Copyright (c) 2024, Tri Dao.
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# Adapted from https://github.com/Dao-AILab/causal-conv1d/blob/main/causal_conv1d/causal_conv1d_interface.py
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# and https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/mamba/ops/causal_conv1d.py
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# mypy: ignore-errors
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from typing import Optional, Union
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import torch
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import torch.nn.functional as F
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import triton
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import triton.language as tl
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PAD_SLOT_ID = -1
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def causal_conv1d_ref(
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x: torch.Tensor,
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weight: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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initial_states: Optional[torch.Tensor] = None,
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return_final_states: bool = False,
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final_states_out: Optional[torch.Tensor] = None,
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activation: Optional[str] = "silu",
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):
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"""
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x: (batch, dim, seqlen)
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weight: (dim, width)
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bias: (dim,)
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initial_states: (batch, dim, width - 1)
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final_states_out: (batch, dim, width - 1)
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out: (batch, dim, seqlen)
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"""
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if activation not in [None, "silu", "swish"]:
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raise NotImplementedError("activation must be None, silu, or swish")
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dtype_in = x.dtype
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x = x.to(weight.dtype)
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seqlen = x.shape[-1]
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dim, width = weight.shape
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if initial_states is None:
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out = F.conv1d(x,
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weight.unsqueeze(1),
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bias,
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padding=width - 1,
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groups=dim)
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else:
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x = torch.cat([initial_states, x], dim=-1)
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out = F.conv1d(x, weight.unsqueeze(1), bias, padding=0, groups=dim)
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out = out[..., :seqlen]
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if return_final_states:
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final_states = F.pad(x, (width - 1 - x.shape[-1], 0)).to(
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dtype_in) # (batch, dim, width - 1)
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if final_states_out is not None:
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final_states_out.copy_(final_states)
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else:
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final_states_out = final_states
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out = (out if activation is None else F.silu(out)).to(dtype=dtype_in)
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return (out, None) if not return_final_states else (out, final_states_out)
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def causal_conv1d_fn(
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x: torch.Tensor,
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weight: torch.Tensor,
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bias: Optional[torch.Tensor] = None,
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query_start_loc: Optional[torch.Tensor] = None,
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cache_indices: Optional[torch.Tensor] = None,
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has_initial_state: Optional[torch.Tensor] = None,
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conv_states: Optional[torch.Tensor] = None,
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activation: Optional[str] = "silu",
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pad_slot_id: int = PAD_SLOT_ID,
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):
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"""
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x: (batch, dim, seqlen) or (dim,cu_seq_len) for varlen
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sequences are concatenated from left to right for varlen
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weight: (dim, width)
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bias: (dim,)
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query_start_loc: (batch + 1) int32
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The cumulative sequence lengths of the sequences in
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the batch, used to index into sequence. prepended by 0.
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for example: query_start_loc = torch.Tensor([0,10,16,17]),
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x.shape=(dim,17)
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cache_indices: (batch) int32
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indicates the corresponding state index,
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like so: conv_state = conv_states[cache_indices[batch_id]]
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has_initial_state: (batch) bool
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indicates whether should the kernel take the current state as initial
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state for the calculations
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conv_states: (...,dim,width - 1) itype
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updated inplace if provided
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activation: either None or "silu" or "swish"
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pad_slot_id: int
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if cache_indices is passed, lets the kernel identify padded
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entries that will not be processed,
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for example: cache_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, seqlen)
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"""
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if activation not in [None, "silu", "swish"]:
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raise NotImplementedError("activation must be None, silu, or swish")
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if x.stride(-1) != 1:
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x = x.contiguous()
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bias = bias.contiguous() if bias is not None else None
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out_ref = []
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out_ref_b = []
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seqlens = query_start_loc[1:] - query_start_loc[:-1]
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seqlens = seqlens.tolist()
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splits = torch.split(x, seqlens, dim=-1)
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for i in range(len(seqlens)):
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x_s = splits[i]
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if cache_indices[i] == PAD_SLOT_ID:
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continue
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out_ref_b.append(
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causal_conv1d_ref(
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x_s,
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weight,
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bias,
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activation=activation,
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return_final_states=True,
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final_states_out=conv_states[cache_indices[i]].unsqueeze(0),
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initial_states=conv_states[cache_indices[i]]
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if has_initial_state[i] else None))
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out_ref.append(torch.cat([t[0] for t in out_ref_b], dim=-1))
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out_ref_tensor = torch.cat(out_ref, dim=0)
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return out_ref_tensor
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@triton.jit()
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def _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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cache_seqlens_ptr, # circular buffer
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conv_state_indices_ptr,
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num_accepted_tokens_ptr,
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intermediate_conv_window_ptr,
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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_inter_seq: tl.constexpr,
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stride_inter_step: tl.constexpr,
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stride_inter_dim: tl.constexpr,
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stride_inter_win: 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_CONTINUOUS_BATCHING: 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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SAVE_INTERMEDIATE: 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_CONTINUOUS_BATCHING:
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# mask = idx_seq < batch
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conv_state_batch_coord = tl.load(conv_state_indices_ptr +
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idx_seq * stride_state_indices).to(
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tl.int64)
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else:
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conv_state_batch_coord = idx_seq
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if USE_PAD_SLOT: # noqa
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if conv_state_batch_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_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 = tl.load(num_accepted_tokens_ptr +
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idx_seq) - 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_state_batch_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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# STEP 2: assume state_len > seqlen
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idx_tokens = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
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# The conv_state updates works in a sliding window manner,
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# 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_state_batch_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) * stride_conv_state_tok)[:, None]
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) # [BLOCK_M, BLOCK_N]
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mask = ((conv_state_batch_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 + (idx_seq * stride_x_seq) + (idx_feats * stride_x_dim
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) # [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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conv_state_base = (conv_state_ptr +
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(conv_state_batch_coord * stride_conv_state_seq) +
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(idx_feats * stride_conv_state_dim)) # [BLOCK_N,]
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conv_state_ptrs_target = (conv_state_base +
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(idx_tokens * stride_conv_state_tok)[:, None]
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) # [BLOCK_M, BLOCK_N]
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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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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.static_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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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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if SILU_ACTIVATION:
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acc = acc / (1 + tl.exp(-acc))
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# mask_1d = (idx_token < seqlen) & (
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# idx_feats < dim
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# ) # token-index # feature-index
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maskL = idx_feats < dim
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maskR = tl.full(maskL.shape, False, tl.int1)
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mask_1d = tl.where(idx_token < seqlen, maskL, maskR)
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o_ptrs = (o_ptr + (idx_seq) * stride_o_seq +
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idx_token * stride_o_token + (idx_feats * stride_o_dim))
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tl.store(o_ptrs, acc, mask=mask_1d)
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if SAVE_INTERMEDIATE:
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# Save the window state after consuming this token
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# Layout: [seq(cache line), step, dim, win(K-1)]
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base_ptr = (intermediate_conv_window_ptr +
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conv_state_batch_coord * stride_inter_seq +
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idx_token * stride_inter_step +
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idx_feats * stride_inter_dim)
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if KERNEL_WIDTH >= 2:
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tl.store(base_ptr + 0 * stride_inter_win, col0, mask=mask_w)
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if KERNEL_WIDTH >= 3:
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tl.store(base_ptr + 1 * stride_inter_win, col1, mask=mask_w)
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if KERNEL_WIDTH >= 4:
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tl.store(base_ptr + 2 * stride_inter_win, col2, mask=mask_w)
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def causal_conv1d_update_npu(
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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: Optional[torch.Tensor] = None,
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activation: Union[bool, str, None] = None,
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cache_seqlens: Optional[torch.Tensor] = None,
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conv_state_indices: Optional[torch.Tensor] = None,
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num_accepted_tokens: Optional[torch.Tensor] = None,
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intermediate_conv_window: Optional[torch.Tensor] = None,
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pad_slot_id: int = PAD_SLOT_ID,
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metadata=None,
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validate_data=False,
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):
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"""
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x: (batch, dim) or (batch, dim, seqlen)
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[shape=2: single token prediction]
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[shape=3: single or multiple tokens prediction]
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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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cache_seqlens: (batch,), dtype int32.
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If not None, the conv_state is treated as a circular buffer.
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The conv_state will be updated by copying x to the conv_state
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starting at the index
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@cache_seqlens % state_len.
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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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pad_slot_id: int
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if cache_indices is passed, lets the kernel identify padded
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entries that will not be processed,
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for example: cache_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)
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"""
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if validate_data:
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assert cache_seqlens is None # not implemented yet - ok for vLLM
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assert pad_slot_id is not None
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assert x.stride(1) == 1
|
|
if isinstance(activation, bool):
|
|
activation = "silu" if activation is True else None
|
|
elif activation is not None:
|
|
assert activation in ["silu", "swish"]
|
|
unsqueeze = x.dim() == 2
|
|
if unsqueeze:
|
|
# make it (batch, dim, seqlen) with seqlen == 1
|
|
x = x.unsqueeze(-1)
|
|
batch, dim, seqlen = x.shape
|
|
_, width = weight.shape
|
|
# conv_state: (..., dim, state_len), where state_len >= width - 1
|
|
num_cache_lines, _, state_len = conv_state.size()
|
|
|
|
if validate_data:
|
|
assert dim == weight.size(0)
|
|
assert (
|
|
conv_state.stride(-2) == 1
|
|
), f"ERROR: expect contiguous along feat-dim of conv_state (currently stride={conv_state.stride()})"
|
|
assert state_len >= width - 1
|
|
# 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
|
|
assert cache_seqlens is None # not needed for vLLM - circular buffer
|
|
|
|
# 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()
|
|
|
|
stride_x_seq, stride_x_dim, stride_x_token = x.stride(
|
|
) # X (batch, dim, seqlen)
|
|
|
|
stride_o_seq, stride_o_dim, stride_o_token = out.stride()
|
|
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)
|
|
state_len = width - 1 + (seqlen - 1) # effective state_len needed
|
|
np2_statelen = triton.next_power_of_2(state_len)
|
|
|
|
def grid(META):
|
|
return (
|
|
batch,
|
|
triton.cdiv(dim, META["BLOCK_N"]),
|
|
)
|
|
|
|
# prepare intermediate buffer strides if provided
|
|
if intermediate_conv_window is not None:
|
|
stride_inter_seq, stride_inter_step, stride_inter_dim, stride_inter_win = (
|
|
intermediate_conv_window.stride(0),
|
|
intermediate_conv_window.stride(1),
|
|
intermediate_conv_window.stride(2),
|
|
intermediate_conv_window.stride(3),
|
|
)
|
|
else:
|
|
stride_inter_seq = stride_inter_step = stride_inter_dim = stride_inter_win = 0
|
|
|
|
_causal_conv1d_update_kernel[grid](
|
|
# Pointers to matrices
|
|
x,
|
|
weight,
|
|
bias,
|
|
conv_state,
|
|
cache_seqlens,
|
|
conv_state_indices,
|
|
num_accepted_tokens,
|
|
intermediate_conv_window
|
|
if intermediate_conv_window is not None else x,
|
|
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_inter_seq,
|
|
stride_inter_step,
|
|
stride_inter_dim,
|
|
stride_inter_win,
|
|
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_CONTINUOUS_BATCHING=conv_state_indices 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=128,
|
|
SAVE_INTERMEDIATE=intermediate_conv_window is not None,
|
|
)
|
|
if unsqueeze:
|
|
out = out.squeeze(-1)
|
|
return out
|