290 lines
13 KiB
Python
290 lines
13 KiB
Python
# Copyright (c) 2024, Tri Dao, Albert Gu.
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import math
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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from einops import rearrange
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try:
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from flash_attn import flash_attn_with_kvcache
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except ImportError:
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flash_attn_with_kvcache = None
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try:
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from flash_attn.layers.rotary import RotaryEmbedding
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except ImportError:
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RotaryEmbedding = None
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try:
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from causal_conv1d import causal_conv1d_fn, causal_conv1d_update
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except ImportError:
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causal_conv1d_fn, causal_conv1d_update = None, None
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def _update_kv_cache(kv, inference_params, layer_idx):
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"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
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# Pre-allocate memory for key-values for inference.
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num_heads, head_dim = kv.shape[-2:]
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assert layer_idx in inference_params.key_value_memory_dict
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kv_cache, _ = inference_params.key_value_memory_dict[layer_idx]
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# Adjust key and value for inference
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batch_start = inference_params.batch_size_offset
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batch_end = batch_start + kv.shape[0]
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sequence_start = inference_params.seqlen_offset
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sequence_end = sequence_start + kv.shape[1]
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assert batch_end <= kv_cache.shape[0]
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assert sequence_end <= kv_cache.shape[1]
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assert kv_cache is not None
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kv_cache[batch_start:batch_end, sequence_start:sequence_end, ...] = kv
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return kv_cache[batch_start:batch_end, :sequence_end, ...]
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class MHA(nn.Module):
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"""Multi-head self-attention and cross-attention"""
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def __init__(
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self,
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embed_dim,
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num_heads,
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num_heads_kv=None,
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head_dim=None, # If None, use embed_dim // num_heads
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mlp_dim=0,
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qkv_proj_bias=True,
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out_proj_bias=True,
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softmax_scale=None,
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causal=False,
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layer_idx=None,
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d_conv=0,
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rotary_emb_dim=0,
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rotary_emb_base=10000.0,
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rotary_emb_interleaved=False,
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device=None,
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dtype=None,
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) -> None:
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"""
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num_heads_kv: can be used to toggle MQA / GQA. If None, use num_heads.
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return_residual: whether to return the input x along with the output. This is for
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performance reason: for post-norm architecture, returning the input allows us
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to fuse the backward of nn.Linear with the residual connection.
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"""
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factory_kwargs = {"device": device, "dtype": dtype}
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super().__init__()
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self.embed_dim = embed_dim
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self.layer_idx = layer_idx
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self.d_conv = d_conv
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self.rotary_emb_dim = rotary_emb_dim
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self.softmax_scale = softmax_scale
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self.causal = causal
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self.num_heads = num_heads
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self.num_heads_kv = num_heads_kv if num_heads_kv is not None else num_heads
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assert (
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self.num_heads % self.num_heads_kv == 0
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), "num_heads must be divisible by num_heads_kv"
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if head_dim is None:
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assert self.embed_dim % num_heads == 0, "embed_dim must be divisible by num_heads"
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self.head_dim = head_dim if head_dim is not None else self.embed_dim // num_heads
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self.mlp_dim = math.ceil(mlp_dim / 256) * 256
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qkv_dim = self.head_dim * (self.num_heads + 2 * self.num_heads_kv)
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out_dim = self.head_dim * self.num_heads
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if self.rotary_emb_dim > 0:
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assert RotaryEmbedding is not None, "rotary requires flash_attn to be installed"
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self.rotary_emb = RotaryEmbedding(
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self.rotary_emb_dim,
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base=rotary_emb_base,
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interleaved=rotary_emb_interleaved,
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device=device,
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)
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self.in_proj = nn.Linear(embed_dim, qkv_dim + self.mlp_dim, bias=qkv_proj_bias, **factory_kwargs)
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if self.d_conv > 0:
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self.conv1d = nn.Conv1d(
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qkv_dim, qkv_dim, kernel_size=self.d_conv, padding=self.d_conv - 1, groups=qkv_dim,
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**factory_kwargs
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)
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self.out_proj = nn.Linear(out_dim + self.mlp_dim // 2, embed_dim, bias=out_proj_bias, **factory_kwargs)
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def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None):
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dtype = self.out_proj.weight.dtype if dtype is None else dtype
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device = self.out_proj.weight.device
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if self.d_conv > 0:
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conv_state = torch.zeros(
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batch_size, self.conv1d.weight.shape[0], self.d_conv, device=device, dtype=dtype
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)
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else:
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conv_state = None
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kv_cache = torch.empty(
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batch_size, max_seqlen, 2, self.num_heads_kv, self.head_dim, dtype=dtype, device=device,
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)
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return kv_cache, conv_state
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def _update_kv_cache(self, kv, inference_params):
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"""kv: (batch_size, seqlen, 2, nheads, head_dim) or (batch_size, 1, 2, nheads, head_dim)"""
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assert self.layer_idx is not None, "Generation requires layer_idx in the constructor"
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return _update_kv_cache(kv, inference_params, self.layer_idx)
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def _apply_rotary_update_kvcache_attention(self, q, kv, inference_params):
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"""
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Fast path that combine 3 steps: apply rotary to Q and K, update kv cache, and apply attention.
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q: (batch_size, seqlen_q, nheads, head_dim)
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kv: (batch_size, seqlen_k, 2, nheads_kv, head_dim)
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"""
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assert inference_params is not None and inference_params.seqlen_offset > 0
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if self.rotary_emb_dim > 0:
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self.rotary_emb._update_cos_sin_cache(
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inference_params.max_seqlen, device=q.device, dtype=q.dtype
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)
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rotary_cos, rotary_sin = self.rotary_emb._cos_cached, self.rotary_emb._sin_cached
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else:
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rotary_cos, rotary_sin = None, None
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batch = q.shape[0]
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kv_cache, _ = inference_params.key_value_memory_dict[self.layer_idx]
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kv_cache = kv_cache[:batch]
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cache_seqlens = (
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inference_params.lengths_per_sample[:batch]
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if inference_params.lengths_per_sample is not None
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else inference_params.seqlen_offset
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)
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assert flash_attn_with_kvcache is not None, "flash_attn must be installed"
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context = flash_attn_with_kvcache(
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q,
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kv_cache[:, :, 0],
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kv_cache[:, :, 1],
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kv[:, :, 0],
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kv[:, :, 1],
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rotary_cos=rotary_cos,
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rotary_sin=rotary_sin,
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cache_seqlens=cache_seqlens,
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softmax_scale=self.softmax_scale,
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causal=self.causal,
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rotary_interleaved=self.rotary_emb.interleaved if self.rotary_emb_dim > 0 else False,
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)
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return context
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def _update_kvcache_attention(self, q, kv, inference_params):
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"""Write kv to inference_params, then do attention"""
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if (
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inference_params.seqlen_offset == 0
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or flash_attn_with_kvcache is None
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):
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# TODO: this only uses seqlen_offset and not lengths_per_sample.
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kv = self._update_kv_cache(kv, inference_params)
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k, v = kv.unbind(dim=-3)
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return F.scaled_dot_product_attention(
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q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=self.causal, scale=self.softmax_scale
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).transpose(1, 2)
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else:
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batch = q.shape[0]
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kv_cache = inference_params.key_value_memory_dict[self.layer_idx][:batch]
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cache_seqlens = (
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inference_params.lengths_per_sample[:batch]
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if inference_params.lengths_per_sample is not None
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else inference_params.seqlen_offset
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)
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return flash_attn_with_kvcache(
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q,
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kv_cache[:, :, 0],
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kv_cache[:, :, 1],
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kv[:, :, 0],
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kv[:, :, 1],
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cache_seqlens=cache_seqlens,
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softmax_scale=self.softmax_scale,
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causal=self.causal,
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)
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def forward(self, x, inference_params=None):
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"""
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Arguments:
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x: (batch, seqlen, hidden_dim) (where hidden_dim = num heads * head dim) if
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cu_seqlens is None and max_seqlen is None, else (total, hidden_dim) where total
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is the is the sum of the sequence lengths in the batch.
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inference_params: for generation. Adapted from Megatron-LM (and Apex)
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https://github.com/NVIDIA/apex/blob/3ff1a10f72ec07067c4e44759442329804ac5162/apex/transformer/testing/standalone_transformer_lm.py#L470
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"""
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if inference_params is not None and self.layer_idx not in inference_params.key_value_memory_dict:
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inference_params.key_value_memory_dict[self.layer_idx] = self.allocate_inference_cache(
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x.shape[0], inference_params.max_seqlen, dtype=x.dtype
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)
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seqlen_offset = (
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0
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if inference_params is None
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else (
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inference_params.lengths_per_sample
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if inference_params.lengths_per_sample is not None
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else inference_params.seqlen_offset
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)
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)
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rotary_max_seqlen = inference_params.max_seqlen if inference_params is not None else None
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qkv = self.in_proj(x)
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if self.mlp_dim > 0:
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qkv, x_mlp = qkv.split([qkv.shape[-1] - self.mlp_dim, self.mlp_dim], dim=-1)
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x_mlp_up, x_mlp_gate = x_mlp.chunk(2, dim=-1)
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x_mlp = x_mlp_up * F.silu(x_mlp_gate)
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if self.d_conv > 0:
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# The inference code for conv1d is pretty messy, should clean it up
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if (inference_params is None or inference_params.seqlen_offset == 0):
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if causal_conv1d_fn is None:
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qkv = rearrange(
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self.conv1d(rearrange(qkv, "b s d -> b d s"))[..., :-(self.d_conv - 1)], "b d s -> b s d"
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).contiguous()
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else:
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qkv = causal_conv1d_fn(
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qkv.transpose(1, 2),
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rearrange(self.conv1d.weight, "d 1 w -> d w"),
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self.conv1d.bias
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).transpose(1, 2)
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if inference_params is not None:
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_, conv_state = inference_params.key_value_memory_dict[self.layer_idx]
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# If we just take qkv[:, :, -self.d_conv :], it will error if seqlen < self.d_conv
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# Instead F.pad will pad with zeros if seqlen < self.d_conv, and truncate otherwise.
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qkv_t = rearrange(qkv, "b l d -> b d l")
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conv_state.copy_(F.pad(qkv_t, (self.d_conv - qkv_t.shape[-1], 0))) # Update state (B D W)
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else:
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_, conv_state = inference_params.key_value_memory_dict[self.layer_idx]
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assert qkv.shape[1] == 1, "Only support decoding with 1 token at a time for now"
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qkv = qkv.squeeze(1)
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# Conv step
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if causal_conv1d_update is None:
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conv_state.copy_(torch.roll(conv_state, shifts=-1, dims=-1)) # Update state (B D W)
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conv_state[:, :, -1] = qkv
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qkv = torch.sum(conv_state * rearrange(self.conv1d.weight, "d 1 w -> d w"), dim=-1) # (B D)
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if self.conv1d.bias is not None:
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qkv = qkv + self.conv1d.bias
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else:
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qkv = causal_conv1d_update(
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qkv,
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conv_state,
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rearrange(self.conv1d.weight, "d 1 w -> d w"),
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self.conv1d.bias
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)
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qkv = qkv.unsqueeze(1)
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q, kv = qkv.split([self.num_heads * self.head_dim, self.num_heads_kv * 2 * self.head_dim], dim=-1)
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q = rearrange(q, "... (h d) -> ... h d", d=self.head_dim)
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kv = rearrange(kv, "... (two hkv d) -> ... two hkv d", two=2, d=self.head_dim)
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if (
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inference_params is None
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or inference_params.seqlen_offset == 0
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or (self.rotary_emb_dim == 0 or self.rotary_emb_dim % 16 != 0)
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):
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if self.rotary_emb_dim > 0:
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q, kv = self.rotary_emb(
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q, kv, seqlen_offset=seqlen_offset, max_seqlen=rotary_max_seqlen
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)
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if inference_params is None:
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k, v = kv.unbind(dim=-3)
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context = F.scaled_dot_product_attention(
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q.transpose(1, 2), k.transpose(1, 2), v.transpose(1, 2), is_causal=self.causal, scale=self.softmax_scale
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).transpose(1, 2)
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else:
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context = self._update_kvcache_attention(q, kv, inference_params)
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else:
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context = self._apply_rotary_update_kvcache_attention(q, kv, inference_params)
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context = rearrange(context, "... h d -> ... (h d)")
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if self.mlp_dim > 0:
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context = torch.cat([context, x_mlp], dim=-1)
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out = self.out_proj(context)
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return out
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