78bf211539
### What this PR does / why we need it?
Support triton causal_conv1d_fn ops.
### Does this PR introduce _any_ user-facing change?
No.
### How was this patch tested?
CI passed with new added/existing test.
- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c
---------
Signed-off-by: QilaiZhang <245706640@qq.com>
1349 lines
51 KiB
Python
1349 lines
51 KiB
Python
# adapted from vllm/model_executor/layers/mamba/ops/causal_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 Any, Optional, Union
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import torch
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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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@triton.jit()
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def _causal_conv1d_fwd_kernel( # continuous batching
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# Pointers to matrices
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x_ptr, # (dim, cu_seqlen) holding `batch` of actual sequences + padded sequences
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w_ptr, # (dim, width)
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bias_ptr,
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conv_states_ptr,
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conv_state_indices_ptr,
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has_initial_states_ptr,
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query_start_loc_ptr,
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batch_ptr,
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token_chunk_offset_ptr,
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o_ptr, # (dim, seqlen)
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# Matrix dimensions
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dim: tl.constexpr,
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state_len: int,
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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_dim: tl.constexpr, # stride to get to next feature-value,
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stride_x_token: tl.constexpr, # stride to get to next token
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stride_w_dim: tl.constexpr, # stride to get to next dim-axis value
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stride_w_width: tl.constexpr, # stride to get to next width-axis value
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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_cache_indices: 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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HAS_INITIAL_STATES: tl.constexpr,
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IS_CONTINUOUS_BATCHING: tl.constexpr,
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USE_PAD_SLOT: tl.constexpr,
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NP2_STATELEN: tl.constexpr,
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BLOCK_M: tl.constexpr,
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BLOCK_N: tl.constexpr,
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):
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# single-sequence id
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idx_seq = tl.load(batch_ptr + tl.program_id(0)).to(tl.int64)
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chunk_offset = tl.load(token_chunk_offset_ptr + tl.program_id(0))
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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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sequence_start_index = tl.load(query_start_loc_ptr + idx_seq)
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sequence_end_index = tl.load(query_start_loc_ptr + idx_seq + 1)
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# find the actual sequence length
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seqlen = sequence_end_index - sequence_start_index
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token_offset = BLOCK_M * chunk_offset
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segment_len = min(BLOCK_M, seqlen - token_offset)
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# base of the sequence
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x_base = x_ptr + sequence_start_index * stride_x_token + idx_feats * stride_x_dim # [BLOCK_N,]
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if IS_CONTINUOUS_BATCHING:
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# cache_idx
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conv_state_batch_coord = tl.load(conv_state_indices_ptr +
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idx_seq * stride_cache_indices).to(
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tl.int64)
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else:
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# cache_idx
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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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conv_states_base = conv_states_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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w_base = w_ptr + (idx_feats * stride_w_dim) # [BLOCK_N,]
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load_init_state = False
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if HAS_INITIAL_STATES: # the new HAS_INITIAL_STATES
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load_init_state = tl.load(has_initial_states_ptr + idx_seq)
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mask_dim = idx_feats < dim
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# read prior-token data from `x`
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offset_x = token_offset - KERNEL_WIDTH + 1
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if KERNEL_WIDTH >= 2:
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x0_ptrs = x_base + offset_x * stride_x_token
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x0 = tl.load(x0_ptrs, mask_dim & (offset_x > 0))
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if KERNEL_WIDTH >= 3:
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x1_ptrs = x0_ptrs + 1 * stride_x_token
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x1 = tl.load(x1_ptrs, mask_dim & (offset_x + 1 > 0))
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if KERNEL_WIDTH >= 4:
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x2_ptrs = x1_ptrs + 1 * stride_x_token
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x2 = tl.load(x2_ptrs, mask_dim & (offset_x + 2 > 0))
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if load_init_state & (chunk_offset == 0):
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# load from conv_states
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offset_conv_state = state_len - KERNEL_WIDTH + 1
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if KERNEL_WIDTH >= 2:
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x0_ptrs = conv_states_base + offset_conv_state * stride_conv_state_tok
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x0 = tl.load(x0_ptrs, mask_dim, 0.0)
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if KERNEL_WIDTH >= 3:
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x1_ptrs = x0_ptrs + 1 * stride_conv_state_tok
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x1 = tl.load(x1_ptrs, mask_dim)
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if KERNEL_WIDTH >= 4:
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x2_ptrs = x1_ptrs + 1 * stride_conv_state_tok
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x2 = tl.load(x2_ptrs, mask_dim)
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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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x_base_1d = x_base + token_offset * stride_x_token # starting of chunk
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# PRE-LOAD WEIGHTS
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mask_dim = 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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w0 = tl.load(w_ptrs, mask_dim, other=0.0)
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w_ptrs = w_base + (1 * stride_w_width) # [BLOCK_N] tensor
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w1 = tl.load(w_ptrs, mask_dim, 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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w2 = tl.load(w_ptrs, mask_dim, 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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w3 = tl.load(w_ptrs, mask_dim, other=0.0)
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for idx_token in tl.static_range(BLOCK_M):
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acc = acc_preload
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mask_1d = (idx_token
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< segment_len) & mask_dim # token-index # feature-index
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x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
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x = tl.load(x_ptrs_1d, mask=mask_1d)
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if KERNEL_WIDTH == 2:
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acc += x0 * w0 + x * w1
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x0 = x
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elif KERNEL_WIDTH == 3:
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acc += x0 * w0 + x1 * w1 + x * w2
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x0 = x1
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x1 = x
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elif KERNEL_WIDTH == 4:
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acc += x0 * w0 + x1 * w1 + x2 * w2 + x * w3
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x0 = x1
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x1 = x2
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x2 = x
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if SILU_ACTIVATION:
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acc = acc / (1 + tl.exp(-acc))
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o_ptrs = o_ptr + (sequence_start_index + token_offset + idx_token
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) * stride_o_token + (idx_feats * stride_o_dim)
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tl.store(o_ptrs, acc, mask=mask_1d)
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# update conv_state with new data [only by the Triton program handles chunk_offset=0]
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if chunk_offset == 0:
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if state_len <= seqlen: # SMALL_CACHE=True (only move part of 'x' into conv_state cache)
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# just read from 'x'
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# copy 'x' data to conv_state
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# load only 'x' data (and set 0 before 'x' if seqlen < state_len)
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idx_tokens_last = (seqlen - state_len) + tl.arange(
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0, NP2_STATELEN) # [BLOCK_M]
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x_ptrs = x_ptr + (
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(sequence_start_index + idx_tokens_last) *
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stride_x_token)[:, None] + (
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idx_feats * stride_x_dim)[None, :] # [BLOCK_M,BLOCK_N,]
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mask_x = ((idx_tokens_last >= 0)[:, None] &
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(idx_tokens_last < seqlen)[:, None] &
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(idx_feats < dim)[None, :]
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) # token-index # token-index # feature-index
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new_conv_state = tl.load(x_ptrs, mask_x, 0.0)
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idx_tokens_conv = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
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conv_states_ptrs_target = conv_states_base[None, :] + (
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idx_tokens_conv * stride_conv_state_tok)[:, None]
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mask = (idx_tokens_conv < state_len)[:, None] & (idx_feats
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< dim)[None, :]
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tl.debug_barrier()
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tl.store(conv_states_ptrs_target, new_conv_state, mask)
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elif load_init_state:
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# update conv_state by shifting left, i.e. take last few cols from conv_state + cols from 'x'
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idx_tokens_conv = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
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conv_states_ptrs_source = (
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conv_states_ptr +
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(conv_state_batch_coord * stride_conv_state_seq) +
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(idx_feats * stride_conv_state_dim)[None, :] +
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((idx_tokens_conv + seqlen) * 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_conv + seqlen) < state_len)[:, None]
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& (idx_feats < dim)[None, :])
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conv_state = tl.load(conv_states_ptrs_source, mask, other=0.0)
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VAL = state_len - seqlen
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x_ptrs = x_base[None, :] + (
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(idx_tokens_conv - VAL) *
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stride_x_token)[:, None] # [BLOCK_M, BLOCK_N]
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mask_x = ((idx_tokens_conv - VAL >= 0)[:, None] &
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(idx_tokens_conv - 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(
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mask, conv_state, loaded_x
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) # BUG in 'tl.where' which requires a barrier before this
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conv_states_ptrs_target = conv_states_base + (
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idx_tokens_conv *
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stride_conv_state_tok)[:, None] # [BLOCK_M, BLOCK_N]
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mask = (idx_tokens_conv < state_len)[:, None] & (idx_feats
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< dim)[None, :]
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tl.store(conv_states_ptrs_target, new_conv_state, mask)
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else:
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# update conv_state by shifting left, BUT
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# set cols prior to 'x' as zeros + cols from 'x'
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idx_tokens_conv = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
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VAL = state_len - seqlen
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x_ptrs = x_base[None, :] + (
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(idx_tokens_conv - VAL) *
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stride_x_token)[:, None] # [BLOCK_M, BLOCK_N]
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mask_x = ((idx_tokens_conv - VAL >= 0)[:, None] &
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(idx_tokens_conv - 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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new_conv_state = tl.load(x_ptrs, mask_x, 0.0)
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conv_states_ptrs_target = conv_states_base + (
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idx_tokens_conv *
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stride_conv_state_tok)[:, None] # [BLOCK_M, BLOCK_N]
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mask = (idx_tokens_conv < state_len)[:, None] & (idx_feats
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< dim)[None, :]
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tl.debug_barrier()
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tl.store(conv_states_ptrs_target, new_conv_state, mask)
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def causal_conv1d_fn(x: torch.Tensor,
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weight: torch.Tensor,
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bias: Union[torch.Tensor, None],
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conv_states: torch.Tensor,
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query_start_loc: torch.Tensor,
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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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activation: Optional[str] = "silu",
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pad_slot_id: int = PAD_SLOT_ID,
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metadata: Optional[Any] = None,
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validate_data=False):
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"""support varlen + continuous batching when x is 2D tensor
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x: (dim,cu_seq_len)
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cu_seq_len = total tokens of all seqs in that batch
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sequences are concatenated from left to right for varlen
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weight: (dim, width)
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conv_states: (...,dim,width - 1) itype
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updated inplace if provided
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[it use `cache_indices` to get the index to the cache of conv_state for that sequence
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conv_state[cache_indices[i]] for seq-i - to be used as initial_state when has_initial_state[i] = True
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and after that conv_state[cache_indices[i]] need to be shift-left and updated with values from 'x'
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]
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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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if
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x = [5, 1, 1, 1] <- continuous batching (batch=4)
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then
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query_start_loc = [0, 5, 6, 7, 8] <- the starting index of the next sequence; while the last value is
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the ending index of the last sequence
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[length(query_start_loc)-1 == batch]
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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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[single boolean for each sequence in the batch: True or False]
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bias: (dim,)
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activation: either None or "silu" or "swish" or True
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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: same shape as `x`
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"""
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if isinstance(activation, bool) and activation:
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activation = "silu"
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# Store original dtype to cast back at the end
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out = torch.empty_strided(x.size(),
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x.stride(),
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dtype=x.dtype,
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device=x.device)
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dim, _ = x.shape
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_, width = weight.shape
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state_len = width - 1
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np2_statelen = triton.next_power_of_2(state_len)
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padded_batch = query_start_loc.size(0) - 1
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stride_x_dim = x.stride(0)
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stride_x_token = x.stride(1)
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stride_w_dim = weight.stride(0)
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stride_w_width = weight.stride(1)
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stride_istate_seq = 0
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stride_istate_dim = 0
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stride_istate_token = 0
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stride_o_dim = out.stride(0)
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stride_o_token = out.stride(1)
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num_cache_lines = 0
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if conv_states is not None:
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# extensions to support vLLM:
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# 1. conv_states is used to replaced initial_states
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# 2. conv_states serve as a cache with num cache lines can be larger than batch size
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# 3. mapping from sequence x[idx] to a cache line at index as specified via cache_indices[idx]
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# 4. computation can be skipped if cache_indices[idx] == pad_slot_id
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num_cache_lines = conv_states.size(0)
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stride_istate_seq = conv_states.stride(0)
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stride_istate_dim = conv_states.stride(1)
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stride_istate_token = conv_states.stride(2)
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stride_cache_indices = cache_indices.stride(
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0) if cache_indices is not None else 0
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if validate_data:
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is_channel_last = (x.stride(0) == 1) & (x.stride(1) > 1)
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assert x.dim() == 2
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assert width in [2, 3, 4]
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assert query_start_loc is not None
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assert query_start_loc.dim() == 1
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assert x.stride(0) == 1 or x.stride(1) == 1
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if bias is not None:
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assert bias.dim() == 1
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assert dim == bias.size(0)
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if conv_states is not None:
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assert (num_cache_lines == conv_states.shape[0]
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and dim == conv_states.shape[1]
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and conv_states.shape[2] >= width - 1)
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assert stride_istate_dim == 1
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if cache_indices is not None:
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assert cache_indices.dim() == 1
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assert padded_batch == cache_indices.size(0)
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if has_initial_state is not None:
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assert has_initial_state.size() == (padded_batch, )
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assert conv_states is not None, "ERROR: `has_initial_state` is used, which needs also `conv_states`"
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assert weight.stride(1) == 1
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assert (dim, width) == weight.shape
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assert is_channel_last, "Need to run in channel-last layout"
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BLOCK_M = 64
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seqlens = query_start_loc.diff()
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seq_blocks = -(-seqlens // BLOCK_M)
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total_seq_blocks = seq_blocks.sum().item()
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# tracking which seq-idx the Triton program is handling
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batch_ptr = torch.repeat_interleave(
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torch.arange(len(seq_blocks), device=x.device),
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seq_blocks).to(torch.int32)
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# tracking BLOCK_M-based index in the sequence the Triton program is handling
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max_blocks = seq_blocks.max().item() if len(seq_blocks) > 0 else 0
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arange = torch.arange(max_blocks, device=x.device)
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mask = arange.unsqueeze(0) < seq_blocks.unsqueeze(1)
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token_chunk_offset_ptr = arange.repeat(len(seq_blocks),
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1)[mask].to(torch.int32)
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BLOCK_N = 256
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grid = (total_seq_blocks, triton.cdiv(dim, BLOCK_N))
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with torch.npu.device(x.device.index):
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_causal_conv1d_fwd_kernel[grid](
|
|
# Pointers to matrices
|
|
x,
|
|
weight,
|
|
bias,
|
|
conv_states,
|
|
cache_indices,
|
|
has_initial_state,
|
|
query_start_loc,
|
|
batch_ptr,
|
|
token_chunk_offset_ptr,
|
|
out,
|
|
# Matrix dimensions
|
|
dim,
|
|
state_len,
|
|
num_cache_lines,
|
|
# stride
|
|
stride_x_dim,
|
|
stride_x_token,
|
|
stride_w_dim,
|
|
stride_w_width,
|
|
stride_istate_seq,
|
|
stride_istate_dim,
|
|
stride_istate_token,
|
|
stride_cache_indices,
|
|
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"],
|
|
HAS_INITIAL_STATES=has_initial_state is not None,
|
|
IS_CONTINUOUS_BATCHING=cache_indices is not None,
|
|
USE_PAD_SLOT=pad_slot_id is not None,
|
|
NP2_STATELEN=np2_statelen,
|
|
BLOCK_M=BLOCK_M,
|
|
BLOCK_N=BLOCK_N)
|
|
|
|
return out
|
|
|
|
|
|
# TODO copied from vllm and it needs to be optimized
|
|
@triton.jit()
|
|
def _original_causal_conv1d_update_kernel(
|
|
# Pointers to matrices
|
|
x_ptr, # (batch, dim, seqlen)
|
|
w_ptr, # (dim, width)
|
|
bias_ptr,
|
|
conv_state_ptr,
|
|
conv_state_indices_ptr,
|
|
num_accepted_tokens_ptr,
|
|
query_start_loc_ptr, # (batch + 1)
|
|
block_idx_last_scheduled_token, # (batch,)
|
|
initial_state_idx, # (batch,)
|
|
o_ptr, # (batch, dim, seqlen)
|
|
# Matrix dimensions
|
|
batch: int,
|
|
dim: tl.constexpr,
|
|
seqlen: tl.constexpr,
|
|
state_len: tl.constexpr,
|
|
num_cache_lines: tl.constexpr, # added to support vLLM larger cache lines
|
|
# Strides
|
|
stride_x_seq: tl.constexpr,
|
|
stride_x_dim: tl.constexpr,
|
|
stride_x_token: tl.constexpr,
|
|
stride_w_dim: tl.constexpr,
|
|
stride_w_width: tl.constexpr,
|
|
stride_conv_state_seq: tl.constexpr,
|
|
stride_conv_state_dim: tl.constexpr,
|
|
stride_conv_state_tok: tl.constexpr,
|
|
stride_state_indices: tl.constexpr,
|
|
stride_o_seq: tl.constexpr,
|
|
stride_o_dim: tl.constexpr,
|
|
stride_o_token: tl.constexpr,
|
|
# others
|
|
pad_slot_id: tl.constexpr,
|
|
# Meta-parameters
|
|
HAS_BIAS: tl.constexpr,
|
|
KERNEL_WIDTH: tl.constexpr,
|
|
SILU_ACTIVATION: tl.constexpr,
|
|
IS_VARLEN: tl.constexpr,
|
|
IS_APC_ENABLED: tl.constexpr,
|
|
IS_SPEC_DECODING: tl.constexpr,
|
|
NP2_STATELEN: tl.constexpr,
|
|
USE_PAD_SLOT: tl.constexpr,
|
|
BLOCK_N: tl.constexpr,
|
|
):
|
|
# ruff: noqa: E501
|
|
idx_seq = tl.program_id(0)
|
|
if idx_seq >= batch:
|
|
return
|
|
|
|
# [BLOCK_N,] elements along the feature-dimension (channel)
|
|
idx_feats = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
|
|
|
|
if IS_APC_ENABLED:
|
|
# Get the state from the initial_state_idx
|
|
conv_state_init = tl.load(initial_state_idx + idx_seq)
|
|
current_last_index = tl.load(block_idx_last_scheduled_token + idx_seq)
|
|
else:
|
|
conv_state_init = 0
|
|
current_last_index = 0
|
|
|
|
# cache_idx
|
|
conv_states_input_coord = tl.load(conv_state_indices_ptr +
|
|
idx_seq * stride_state_indices +
|
|
conv_state_init).to(tl.int64)
|
|
|
|
if USE_PAD_SLOT: # noqa
|
|
if conv_states_input_coord == pad_slot_id:
|
|
# not processing as this is not the actual sequence
|
|
return
|
|
|
|
if IS_VARLEN:
|
|
query_start_index = tl.load(query_start_loc_ptr + idx_seq).to(tl.int64)
|
|
query_end_index = tl.load(query_start_loc_ptr + (idx_seq + 1)).to(
|
|
tl.int64)
|
|
# revise state_len and seqlen
|
|
state_len = state_len - (seqlen -
|
|
(query_end_index - query_start_index))
|
|
seqlen = query_end_index - query_start_index
|
|
x_offset = query_start_index * stride_x_token
|
|
o_offset = query_start_index * stride_o_token
|
|
else:
|
|
query_start_index = idx_seq * seqlen
|
|
query_end_index = query_start_index + seqlen
|
|
x_offset = idx_seq * stride_x_seq
|
|
o_offset = idx_seq * stride_o_seq
|
|
|
|
if query_start_index == query_end_index:
|
|
return
|
|
|
|
if IS_SPEC_DECODING:
|
|
# The rolling of conv state:
|
|
#
|
|
# Before forward, the conv_state is:
|
|
# [history1, history2, ..., historyM].
|
|
#
|
|
# After forward, the conv_state becomes:
|
|
# [history2, ..., historyM, draft1, draft2, ..., draftN].
|
|
#
|
|
# After acceptance, it becomes:
|
|
#
|
|
# - accept 1 tokens: [history2, ..., historyM, draft1]
|
|
# - accept 2 tokens: [history3, ..., historyM, draft1, draft2]
|
|
# - and so on.
|
|
conv_state_token_offset = (
|
|
tl.load(num_accepted_tokens_ptr + idx_seq).to(tl.int64) - 1)
|
|
else:
|
|
conv_state_token_offset = 0
|
|
|
|
# STEP 1: READ init_state data
|
|
conv_states_base = (conv_state_ptr +
|
|
(conv_states_input_coord * stride_conv_state_seq) +
|
|
(idx_feats * stride_conv_state_dim))
|
|
mask_w = idx_feats < dim
|
|
|
|
prior_tokens = conv_states_base + conv_state_token_offset * stride_conv_state_tok
|
|
if KERNEL_WIDTH >= 2:
|
|
conv_states_ptrs = prior_tokens # [BLOCK_N]
|
|
col0 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH >= 3:
|
|
conv_states_ptrs = prior_tokens + 1 * stride_conv_state_tok # [BLOCK_N]
|
|
col1 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH >= 4:
|
|
conv_states_ptrs = prior_tokens + 2 * stride_conv_state_tok # [BLOCK_N]
|
|
col2 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH >= 5:
|
|
conv_states_ptrs = prior_tokens + 3 * stride_conv_state_tok # [BLOCK_N]
|
|
col3 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH >= 6:
|
|
conv_states_ptrs = prior_tokens + 4 * stride_conv_state_tok # [BLOCK_N]
|
|
col4 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
|
|
# STEP 2: assume state_len > seqlen
|
|
idx_tokens = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
|
|
|
|
# With speculative decoding, the conv_state updates works in a sliding
|
|
# window manner, at each forward pass, the tokens are shift by 1, so we
|
|
# load since idx_tokens + 1.
|
|
conv_state_ptrs_source = (
|
|
conv_state_ptr + (conv_states_input_coord * stride_conv_state_seq) +
|
|
conv_state_token_offset * stride_conv_state_tok +
|
|
(idx_feats * stride_conv_state_dim)[None, :] +
|
|
((idx_tokens + (1 if IS_SPEC_DECODING else seqlen)) *
|
|
stride_conv_state_tok)[:, None]) # [BLOCK_M, BLOCK_N]
|
|
mask = ((conv_states_input_coord < num_cache_lines)
|
|
& ((idx_tokens + seqlen) < state_len)[:, None]
|
|
& (idx_feats < dim)[None, :])
|
|
conv_state = tl.load(conv_state_ptrs_source, mask, other=0.0)
|
|
|
|
VAL = state_len - seqlen
|
|
x_base = x_ptr + x_offset + (idx_feats * stride_x_dim) # [BLOCK_N]
|
|
|
|
x_ptrs = (x_base[None, :] + ((idx_tokens - VAL) * stride_x_token)[:, None]
|
|
) # [BLOCK_M, BLOCK_N]
|
|
|
|
mask_x = ((idx_tokens - VAL >= 0)[:, None]
|
|
& (idx_tokens - VAL < seqlen)[:, None]
|
|
& (idx_feats < dim)[None, :]
|
|
) # token-index # token-index # feature-index
|
|
loaded_x = tl.load(x_ptrs, mask_x, 0.0)
|
|
tl.debug_barrier()
|
|
|
|
new_conv_state = tl.where(mask, conv_state, loaded_x)
|
|
|
|
# Get the state from the initial_state_idx
|
|
# cache_idx
|
|
conv_states_offset = tl.load(conv_state_indices_ptr +
|
|
idx_seq * stride_state_indices +
|
|
current_last_index).to(tl.int64)
|
|
conv_state_ptrs_target = (
|
|
conv_state_ptr +
|
|
(conv_states_offset * stride_conv_state_seq) # Offset from seq
|
|
+ (idx_feats * stride_conv_state_dim))[None, :] + ( # [BLOCK_N,]
|
|
idx_tokens * stride_conv_state_tok)[:, None]
|
|
mask = (idx_tokens < state_len)[:, None] & (idx_feats < dim)[None, :]
|
|
tl.store(conv_state_ptrs_target, new_conv_state, mask)
|
|
|
|
# STEP 3: init accumulator
|
|
if HAS_BIAS:
|
|
bias = bias_ptr + idx_feats
|
|
mask_bias = idx_feats < dim
|
|
acc_preload = tl.load(bias, mask=mask_bias,
|
|
other=0.0).to(tl.float32) # [BLOCK_N]
|
|
else:
|
|
acc_preload = tl.zeros((BLOCK_N, ), dtype=tl.float32)
|
|
|
|
# STEP 4:
|
|
# PRE-LOAD WEIGHTS
|
|
# first kernel column, configured for weights to handle BLOCK_N features in range
|
|
w_base = w_ptr + (idx_feats * stride_w_dim) # [BLOCK_N,]
|
|
mask_w = idx_feats < dim
|
|
if KERNEL_WIDTH >= 2:
|
|
w_ptrs = w_base + (0 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col0 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
w_ptrs = w_base + (1 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col1 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
if KERNEL_WIDTH >= 3:
|
|
w_ptrs = w_base + (2 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col2 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
if KERNEL_WIDTH >= 4:
|
|
w_ptrs = w_base + (3 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col3 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
if KERNEL_WIDTH >= 5:
|
|
w_ptrs = w_base + (4 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col4 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
if KERNEL_WIDTH >= 6:
|
|
w_ptrs = w_base + (5 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col5 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
|
|
x_base_1d = x_base # starting of chunk [BLOCK_N]
|
|
mask_x_1d = idx_feats < dim
|
|
|
|
# STEP 5: compute each token
|
|
for idx_token in tl.range(seqlen):
|
|
acc = acc_preload
|
|
|
|
matrix_w = w_col0
|
|
matrix_x = col0
|
|
for j in tl.static_range(KERNEL_WIDTH):
|
|
if KERNEL_WIDTH == 2:
|
|
if j == 1: # KERNEL_WIDTH-1:
|
|
matrix_w = w_col1
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
elif KERNEL_WIDTH == 3:
|
|
if j == 1:
|
|
matrix_w = w_col1
|
|
matrix_x = col1
|
|
elif j == 2:
|
|
matrix_w = w_col2
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
elif KERNEL_WIDTH == 4:
|
|
if j == 1:
|
|
matrix_w = w_col1
|
|
matrix_x = col1
|
|
elif j == 2:
|
|
matrix_w = w_col2
|
|
matrix_x = col2
|
|
elif j == 3:
|
|
matrix_w = w_col3
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
elif KERNEL_WIDTH == 5:
|
|
if j == 1:
|
|
matrix_w = w_col1
|
|
matrix_x = col1
|
|
elif j == 2:
|
|
matrix_w = w_col2
|
|
matrix_x = col2
|
|
elif j == 3:
|
|
matrix_w = w_col3
|
|
matrix_x = col3
|
|
elif j == 4:
|
|
matrix_w = w_col4
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
elif KERNEL_WIDTH == 6:
|
|
if j == 1:
|
|
matrix_w = w_col1
|
|
matrix_x = col1
|
|
elif j == 2:
|
|
matrix_w = w_col2
|
|
matrix_x = col2
|
|
elif j == 3:
|
|
matrix_w = w_col3
|
|
matrix_x = col3
|
|
elif j == 4:
|
|
matrix_w = w_col4
|
|
matrix_x = col4
|
|
elif j == 5:
|
|
matrix_w = w_col5
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
|
|
acc += matrix_x * matrix_w # [BLOCK_N]
|
|
|
|
if KERNEL_WIDTH == 2:
|
|
col0 = matrix_x
|
|
elif KERNEL_WIDTH == 3:
|
|
col0 = col1
|
|
col1 = matrix_x
|
|
elif KERNEL_WIDTH == 4:
|
|
col0 = col1
|
|
col1 = col2
|
|
col2 = matrix_x
|
|
elif KERNEL_WIDTH == 5:
|
|
col0 = col1
|
|
col1 = col2
|
|
col2 = col3
|
|
col3 = matrix_x
|
|
elif KERNEL_WIDTH == 6:
|
|
col0 = col1
|
|
col1 = col2
|
|
col2 = col3
|
|
col3 = col4
|
|
col4 = matrix_x
|
|
|
|
if SILU_ACTIVATION:
|
|
acc = acc / (1 + tl.exp(-acc))
|
|
mask_1d = (idx_token < seqlen) & (idx_feats < dim
|
|
) # token-index # feature-index
|
|
o_ptrs = (o_ptr + o_offset + idx_token * stride_o_token +
|
|
(idx_feats * stride_o_dim))
|
|
|
|
tl.store(o_ptrs, acc, mask=mask_1d)
|
|
|
|
|
|
# TODO copied from vllm and it needs to be optimized
|
|
def original_causal_conv1d_update(
|
|
x: torch.Tensor,
|
|
conv_state: torch.Tensor,
|
|
weight: torch.Tensor,
|
|
bias: torch.Tensor | None = None,
|
|
activation: bool | str | None = None,
|
|
conv_state_indices: torch.Tensor | None = None,
|
|
num_accepted_tokens: torch.Tensor | None = None,
|
|
query_start_loc: torch.Tensor | None = None,
|
|
max_query_len: int = -1,
|
|
pad_slot_id: int = PAD_SLOT_ID,
|
|
block_idx_last_scheduled_token: torch.Tensor | None = None,
|
|
initial_state_idx: torch.Tensor | None = None,
|
|
validate_data=False,
|
|
):
|
|
"""
|
|
x: Input tensor which can take the following shapes:
|
|
|
|
- `[batch, dim]` - single token prediction
|
|
- `[batch, dim, seqlen]` - single or multiple tokens prediction
|
|
- `[num_tokens, dim]` - continuous batching, where num_tokens is
|
|
the total tokens of all sequences in that batch
|
|
|
|
conv_state: (..., dim, state_len), where state_len >= width - 1
|
|
weight: (dim, width)
|
|
bias: (dim,)
|
|
conv_state_indices: (batch,), dtype int32
|
|
If not None, the conv_state is a larger tensor along the batch dim,
|
|
and we are selecting the batch coords specified by conv_state_indices.
|
|
Useful for a continuous batching scenario.
|
|
block_idx_last_scheduled_token: (batch,), dtype int32
|
|
The pointer into conv_state_indices, where the last cache block to be filled is located.
|
|
initial_state_idx: (batch,), dtype int32
|
|
The pointer into conv_state_indices, where the cache block containing the initial state is located.
|
|
num_accepted_tokens: (batch,), dtype int32
|
|
If not None, it indicates the number of accepted tokens for each
|
|
sequence in the batch.
|
|
This is used in speculative decoding, where the conv_state is updated
|
|
in a sliding window manner.
|
|
query_start_loc: (batch + 1,) int32
|
|
If not None, the inputs is given in a varlen fashion and this indicates
|
|
the starting index of each sequence in the batch.
|
|
max_query_len: int
|
|
If query_start_loc is not None, this indicates the maximum query
|
|
length in the batch.
|
|
pad_slot_id: int
|
|
if conv_state_indices is passed, lets the kernel identify padded
|
|
entries that will not be processed,
|
|
for example: conv_state_indices = [pad_slot_id, 1 ,20 ,pad_slot_id]
|
|
in this case, the kernel will not process entries at
|
|
indices 0 and 3
|
|
out: (batch, dim) or (batch, dim, seqlen) or (num_tokens, dim), same shape as `x`
|
|
"""
|
|
if validate_data:
|
|
assert pad_slot_id is not None
|
|
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"]
|
|
|
|
original_x_dtype = x.dtype
|
|
x = x.to(conv_state.dtype)
|
|
unsqueeze = query_start_loc is None and x.dim() == 2
|
|
if unsqueeze:
|
|
# make it (batch, dim, seqlen) with seqlen == 1
|
|
x = x.unsqueeze(-1)
|
|
if query_start_loc is None:
|
|
batch, dim, seqlen = x.shape
|
|
else:
|
|
assert conv_state_indices is not None
|
|
batch = conv_state_indices.size(0)
|
|
dim = x.size(1)
|
|
seqlen = max_query_len
|
|
_, 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
|
|
|
|
# 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
|
|
x_ptr, # (batch, dim, seqlen)
|
|
w_ptr, # (dim, width)
|
|
bias_ptr,
|
|
conv_state_ptr,
|
|
cache_seqlens_ptr, # circular buffer
|
|
conv_state_indices_ptr,
|
|
num_accepted_tokens_ptr,
|
|
intermediate_conv_window_ptr,
|
|
o_ptr, # (batch, dim, seqlen)
|
|
# Matrix dimensions
|
|
batch: int,
|
|
dim: tl.constexpr,
|
|
seqlen: tl.constexpr,
|
|
state_len: tl.constexpr,
|
|
num_cache_lines: tl.constexpr, # added to support vLLM larger cache lines
|
|
# Strides
|
|
stride_x_seq: tl.constexpr,
|
|
stride_x_dim: tl.constexpr,
|
|
stride_x_token: tl.constexpr,
|
|
stride_w_dim: tl.constexpr,
|
|
stride_w_width: tl.constexpr,
|
|
stride_conv_state_seq: tl.constexpr,
|
|
stride_conv_state_dim: tl.constexpr,
|
|
stride_conv_state_tok: tl.constexpr,
|
|
stride_state_indices: tl.constexpr,
|
|
stride_inter_seq: tl.constexpr,
|
|
stride_inter_step: tl.constexpr,
|
|
stride_inter_dim: tl.constexpr,
|
|
stride_inter_win: tl.constexpr,
|
|
stride_o_seq: tl.constexpr,
|
|
stride_o_dim: tl.constexpr,
|
|
stride_o_token: tl.constexpr,
|
|
# others
|
|
pad_slot_id: tl.constexpr,
|
|
# Meta-parameters
|
|
HAS_BIAS: tl.constexpr,
|
|
KERNEL_WIDTH: tl.constexpr,
|
|
SILU_ACTIVATION: tl.constexpr,
|
|
IS_CONTINUOUS_BATCHING: tl.constexpr,
|
|
IS_SPEC_DECODING: tl.constexpr,
|
|
NP2_STATELEN: tl.constexpr,
|
|
USE_PAD_SLOT: tl.constexpr,
|
|
BLOCK_N: tl.constexpr,
|
|
SAVE_INTERMEDIATE: tl.constexpr,
|
|
):
|
|
# ruff: noqa: E501
|
|
idx_seq = tl.program_id(0)
|
|
if idx_seq >= batch:
|
|
return
|
|
|
|
# [BLOCK_N,] elements along the feature-dimension (channel)
|
|
idx_feats = tl.program_id(1) * BLOCK_N + tl.arange(0, BLOCK_N)
|
|
|
|
if IS_CONTINUOUS_BATCHING:
|
|
# mask = idx_seq < batch
|
|
conv_state_batch_coord = tl.load(conv_state_indices_ptr +
|
|
idx_seq * stride_state_indices).to(
|
|
tl.int64)
|
|
else:
|
|
conv_state_batch_coord = idx_seq
|
|
if USE_PAD_SLOT: # noqa
|
|
if conv_state_batch_coord == pad_slot_id:
|
|
# not processing as this is not the actual sequence
|
|
return
|
|
|
|
if IS_SPEC_DECODING:
|
|
# The rolling of conv state:
|
|
#
|
|
# Before forward, the conv_state is:
|
|
# [history1, history2, ..., historyM].
|
|
#
|
|
# After forward, the conv_state becomes:
|
|
# [history2, ..., historyM, draft1, draft2, ..., draftN].
|
|
#
|
|
# After acceptance, it becomes:
|
|
#
|
|
# - accept 1 tokens: [history2, ..., historyM, draft1]
|
|
# - accept 2 tokens: [history3, ..., historyM, draft1, draft2]
|
|
# - and so on.
|
|
conv_state_token_offset = tl.load(num_accepted_tokens_ptr +
|
|
idx_seq) - 1
|
|
else:
|
|
conv_state_token_offset = 0
|
|
|
|
# STEP 1: READ init_state data
|
|
conv_states_base = (conv_state_ptr +
|
|
(conv_state_batch_coord * stride_conv_state_seq) +
|
|
(idx_feats * stride_conv_state_dim))
|
|
mask_w = idx_feats < dim
|
|
|
|
prior_tokens = conv_states_base + conv_state_token_offset * stride_conv_state_tok
|
|
if KERNEL_WIDTH >= 2:
|
|
conv_states_ptrs = prior_tokens # [BLOCK_N]
|
|
col0 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH >= 3:
|
|
conv_states_ptrs = prior_tokens + 1 * stride_conv_state_tok # [BLOCK_N]
|
|
col1 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH >= 4:
|
|
conv_states_ptrs = prior_tokens + 2 * stride_conv_state_tok # [BLOCK_N]
|
|
col2 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
if KERNEL_WIDTH == 5:
|
|
conv_states_ptrs = prior_tokens + 3 * stride_conv_state_tok # [BLOCK_N]
|
|
#col3 = tl.load(conv_states_ptrs, mask_w, 0.0)
|
|
|
|
# STEP 2: assume state_len > seqlen
|
|
idx_tokens = tl.arange(0, NP2_STATELEN) # [BLOCK_M]
|
|
|
|
# The conv_state updates works in a sliding window manner,
|
|
# at each forward pass, the tokens are shift by 1, so we
|
|
# load since idx_tokens + 1.
|
|
conv_state_ptrs_source = (
|
|
conv_state_ptr + (conv_state_batch_coord * stride_conv_state_seq) +
|
|
conv_state_token_offset * stride_conv_state_tok +
|
|
(idx_feats * stride_conv_state_dim)[None, :] +
|
|
((idx_tokens + 1) * stride_conv_state_tok)[:, None]
|
|
) # [BLOCK_M, BLOCK_N]
|
|
mask = ((conv_state_batch_coord < num_cache_lines)
|
|
& ((idx_tokens + seqlen) < state_len)[:, None]
|
|
& (idx_feats < dim)[None, :])
|
|
conv_state = tl.load(conv_state_ptrs_source, mask, other=0.0)
|
|
|
|
VAL = state_len - seqlen
|
|
x_base = x_ptr + (idx_seq * stride_x_seq) + (idx_feats * stride_x_dim
|
|
) # [BLOCK_N]
|
|
|
|
x_ptrs = (x_base[None, :] + ((idx_tokens - VAL) * stride_x_token)[:, None]
|
|
) # [BLOCK_M, BLOCK_N]
|
|
|
|
mask_x = ((idx_tokens - VAL >= 0)[:, None]
|
|
& (idx_tokens - VAL < seqlen)[:, None]
|
|
& (idx_feats < dim)[None, :]
|
|
) # token-index # token-index # feature-index
|
|
loaded_x = tl.load(x_ptrs, mask_x, 0.0)
|
|
tl.debug_barrier()
|
|
|
|
new_conv_state = tl.where(mask, conv_state, loaded_x)
|
|
|
|
conv_state_base = (conv_state_ptr +
|
|
(conv_state_batch_coord * stride_conv_state_seq) +
|
|
(idx_feats * stride_conv_state_dim)) # [BLOCK_N,]
|
|
conv_state_ptrs_target = (conv_state_base +
|
|
(idx_tokens * stride_conv_state_tok)[:, None]
|
|
) # [BLOCK_M, BLOCK_N]
|
|
mask = (idx_tokens < state_len)[:, None] & (idx_feats < dim)[None, :]
|
|
tl.store(conv_state_ptrs_target, new_conv_state, mask)
|
|
|
|
# STEP 3: init accumulator
|
|
if HAS_BIAS:
|
|
bias = bias_ptr + idx_feats
|
|
mask_bias = idx_feats < dim
|
|
acc_preload = tl.load(bias, mask=mask_bias,
|
|
other=0.0).to(tl.float32) # [BLOCK_N]
|
|
else:
|
|
acc_preload = tl.zeros((BLOCK_N, ), dtype=tl.float32)
|
|
|
|
# STEP 4:
|
|
# PRE-LOAD WEIGHTS
|
|
# first kernel column, configured for weights to handle BLOCK_N features in range
|
|
w_base = w_ptr + (idx_feats * stride_w_dim) # [BLOCK_N,]
|
|
mask_w = idx_feats < dim
|
|
if KERNEL_WIDTH >= 2:
|
|
w_ptrs = w_base + (0 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col0 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
w_ptrs = w_base + (1 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col1 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
if KERNEL_WIDTH >= 3:
|
|
w_ptrs = w_base + (2 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col2 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
if KERNEL_WIDTH >= 4:
|
|
w_ptrs = w_base + (3 * stride_w_width) # [BLOCK_N] tensor
|
|
w_col3 = tl.load(w_ptrs, mask_w, other=0.0)
|
|
|
|
x_base_1d = x_base # starting of chunk [BLOCK_N]
|
|
mask_x_1d = idx_feats < dim
|
|
|
|
# STEP 5: compute each token
|
|
for idx_token in tl.static_range(seqlen):
|
|
acc = acc_preload
|
|
|
|
matrix_w = w_col0
|
|
matrix_x = col0
|
|
for j in tl.static_range(KERNEL_WIDTH):
|
|
if KERNEL_WIDTH == 2:
|
|
if j == 1: # KERNEL_WIDTH-1:
|
|
matrix_w = w_col1
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
elif KERNEL_WIDTH == 3:
|
|
if j == 1:
|
|
matrix_w = w_col1
|
|
matrix_x = col1
|
|
elif j == 2:
|
|
matrix_w = w_col2
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
elif KERNEL_WIDTH == 4:
|
|
if j == 1:
|
|
matrix_w = w_col1
|
|
matrix_x = col1
|
|
elif j == 2:
|
|
matrix_w = w_col2
|
|
matrix_x = col2
|
|
elif j == 3:
|
|
matrix_w = w_col3
|
|
x_ptrs_1d = x_base_1d + idx_token * stride_x_token # [BLOCK_N]
|
|
matrix_x = tl.load(x_ptrs_1d, mask=mask_x_1d)
|
|
|
|
acc += matrix_x * matrix_w # [BLOCK_N]
|
|
|
|
if KERNEL_WIDTH == 2:
|
|
col0 = matrix_x
|
|
elif KERNEL_WIDTH == 3:
|
|
col0 = col1
|
|
col1 = matrix_x
|
|
elif KERNEL_WIDTH == 4:
|
|
col0 = col1
|
|
col1 = col2
|
|
col2 = matrix_x
|
|
|
|
if SILU_ACTIVATION:
|
|
acc = acc / (1 + tl.exp(-acc))
|
|
# mask_1d = (idx_token < seqlen) & (
|
|
# idx_feats < dim
|
|
# ) # token-index # feature-index
|
|
maskL = idx_feats < dim
|
|
maskR = tl.full(maskL.shape, False, tl.int1)
|
|
mask_1d = tl.where(idx_token < seqlen, maskL, maskR)
|
|
|
|
o_ptrs = (o_ptr + (idx_seq) * stride_o_seq +
|
|
idx_token * stride_o_token + (idx_feats * stride_o_dim))
|
|
|
|
tl.store(o_ptrs, acc, mask=mask_1d)
|
|
|
|
if SAVE_INTERMEDIATE:
|
|
# Save the window state after consuming this token
|
|
# Layout: [seq(cache line), step, dim, win(K-1)]
|
|
base_ptr = (intermediate_conv_window_ptr +
|
|
conv_state_batch_coord * stride_inter_seq +
|
|
idx_token * stride_inter_step +
|
|
idx_feats * stride_inter_dim)
|
|
if KERNEL_WIDTH >= 2:
|
|
tl.store(base_ptr + 0 * stride_inter_win, col0, mask=mask_w)
|
|
if KERNEL_WIDTH >= 3:
|
|
tl.store(base_ptr + 1 * stride_inter_win, col1, mask=mask_w)
|
|
if KERNEL_WIDTH >= 4:
|
|
tl.store(base_ptr + 2 * stride_inter_win, col2, mask=mask_w)
|
|
|
|
|
|
def causal_conv1d_update_npu(
|
|
x: torch.Tensor,
|
|
conv_state: torch.Tensor,
|
|
weight: torch.Tensor,
|
|
bias: Optional[torch.Tensor] = None,
|
|
activation: Union[bool, str, None] = None,
|
|
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,
|
|
validate_data=False,
|
|
):
|
|
"""
|
|
x: (batch, dim) or (batch, dim, seqlen)
|
|
[shape=2: single token prediction]
|
|
[shape=3: single or multiple tokens prediction]
|
|
conv_state: (..., dim, state_len), where state_len >= width - 1
|
|
weight: (dim, width)
|
|
bias: (dim,)
|
|
cache_seqlens: (batch,), dtype int32.
|
|
If not None, the conv_state is treated as a circular buffer.
|
|
The conv_state will be updated by copying x to the conv_state
|
|
starting at the index
|
|
@cache_seqlens % state_len.
|
|
conv_state_indices: (batch,), dtype int32
|
|
If not None, the conv_state is a larger tensor along the batch dim,
|
|
and we are selecting the batch coords specified by conv_state_indices.
|
|
Useful for a continuous batching scenario.
|
|
pad_slot_id: int
|
|
if cache_indices is passed, lets the kernel identify padded
|
|
entries that will not be processed,
|
|
for example: cache_indices = [pad_slot_id, 1 ,20 ,pad_slot_id]
|
|
in this case, the kernel will not process entries at
|
|
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
|
|
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
|