kotoba-speech / fam /llm /fast_model.py
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# Copyright (c) Kotoba Technologies, Inc. and affiliates.
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without modification, are permitted
# provided that the following conditions are met:
#
# 1. Redistributions of source code must retain the above copyright notice, this list of
# conditions and the following disclaimer.
#
# 2. Redistributions in binary form must reproduce the above copyright notice, this
# list of conditions and the following disclaimer in the documentation and/or other
# materials provided with the distribution.
#
# 3. Neither the name of the copyright holder nor the names of its contributors
# may be used to endorse or promote products derived from this software without
# specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR
# IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND
# FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
from dataclasses import dataclass
from functools import reduce
from math import gcd
from typing import Optional, Tuple
import torch
import torch.nn as nn
from torch import Tensor
from torch.nn import functional as F
from fam.llm.utils import get_default_dtype
import logging
# Adjust the logging level
logger = logging.getLogger("torch")
logger.setLevel(logging.ERROR)
def find_multiple(n: int, *args: Tuple[int]) -> int:
k = reduce(lambda x, y: x * y // gcd(x, y), args + (1,))
if n % k == 0:
return n
return n + k - (n % k)
@dataclass
class ModelArgs:
block_size: int = 2048
vocab_size: int = 32000
n_layer: int = 32
n_head: int = 32
dim: int = 4096
speaker_emb_dim: int = 256
intermediate_size: int = None
n_local_heads: int = -1
head_dim: int = 64
norm_eps: float = 1e-5
dtype: torch.dtype = torch.bfloat16
def __post_init__(self):
if self.n_local_heads == -1:
self.n_local_heads = self.n_head
if self.intermediate_size is None:
hidden_dim = 4 * self.dim
n_hidden = int(2 * hidden_dim / 3)
self.intermediate_size = find_multiple(n_hidden, 256)
self.head_dim = self.dim // self.n_head
self.dtype = {"float16": torch.float16, "bfloat16": torch.bfloat16}[get_default_dtype()]
@classmethod
def from_name(cls, name: str):
if name in transformer_configs:
return cls(**transformer_configs[name])
# fuzzy search
config = [config for config in transformer_configs if config in str(name).upper() or config in str(name)]
assert len(config) == 1, name
return cls(**transformer_configs[config[0]])
transformer_configs = {
"kotoba-speech-v0.1": dict(
n_layer=24,
n_head=16,
dim=2048,
vocab_size=2562,
),
}
class KVCache(nn.Module):
def __init__(self, max_batch_size, max_seq_length, n_heads, head_dim, dtype):
super().__init__()
cache_shape = (max_batch_size, n_heads, max_seq_length, head_dim)
self.register_buffer("k_cache", torch.zeros(cache_shape, dtype=dtype))
self.register_buffer("v_cache", torch.zeros(cache_shape, dtype=dtype))
def update(self, input_pos, k_val, v_val):
# input_pos: [S], k_val: [B, H, S, D]
assert input_pos.shape[0] == k_val.shape[2]
k_out = self.k_cache
v_out = self.v_cache
k_out[:, :, input_pos] = k_val
v_out[:, :, input_pos] = v_val
return k_out, v_out
class Transformer(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.config = config
self.tok_embeddings = nn.Embedding(config.vocab_size, config.dim)
self.pos_embeddings = nn.Embedding(config.block_size, config.dim)
self.speaker_cond_pos = nn.Linear(config.speaker_emb_dim, config.dim, bias=False)
self.layers = nn.ModuleList(TransformerBlock(config) for _ in range(config.n_layer))
self.norm = RMSNorm(config.dim, eps=config.norm_eps)
self.output = nn.Linear(config.dim, config.vocab_size, bias=False)
self.mask_cache: Optional[Tensor] = None
self.max_batch_size = -1
self.max_seq_length = -1
def setup_spk_cond_mask(self):
self.spk_cond_mask = torch.zeros((2, 1, self.config.dim), dtype=torch.bool)
self.spk_cond_mask[0] = 1
def setup_caches(self, max_batch_size, max_seq_length):
if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
return
head_dim = self.config.dim // self.config.n_head
max_seq_length = find_multiple(max_seq_length, 8)
self.max_seq_length = max_seq_length
self.max_batch_size = max_batch_size
for b in self.layers:
b.attention.kv_cache = KVCache(
max_batch_size, max_seq_length, self.config.n_local_heads, head_dim, dtype=self.config.dtype
)
self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))
def forward(self, idx: Tensor, spk_emb: Tensor, input_pos: Tensor) -> Tensor:
mask = self.causal_mask[None, None, input_pos]
x = (
self.tok_embeddings(idx)
+ self.pos_embeddings(input_pos)
# masking for speaker condition free guidance
+ self.speaker_cond_pos(spk_emb) * self.spk_cond_mask
)
for i, layer in enumerate(self.layers):
x = layer(x, input_pos, mask)
x = self.norm(x)
logits = self.output(x)
return logits
@classmethod
def from_name(cls, name: str):
return cls(ModelArgs.from_name(name))
class TransformerBlock(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.attention = Attention(config)
self.feed_forward = FeedForward(config)
self.ffn_norm = RMSNorm(config.dim, config.norm_eps)
self.attention_norm = RMSNorm(config.dim, config.norm_eps)
def forward(self, x: Tensor, input_pos: Tensor, mask: Tensor) -> Tensor:
h = x + self.attention(self.attention_norm(x), mask, input_pos)
out = h + self.feed_forward(self.ffn_norm(h))
return out
class Attention(nn.Module):
def __init__(self, config: ModelArgs):
super().__init__()
assert config.dim % config.n_head == 0
total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
# key, query, value projections for all heads, but in a batch
self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
self.wo = nn.Linear(config.dim, config.dim, bias=False)
self.kv_cache = None
self.n_head = config.n_head
self.head_dim = config.head_dim
self.n_local_heads = config.n_local_heads
self.dim = config.dim
def forward(
self,
x: Tensor,
mask: Tensor,
input_pos: Optional[Tensor] = None,
) -> Tensor:
bsz, seqlen, _ = x.shape
kv_size = self.n_local_heads * self.head_dim
q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
q = q.view(bsz, seqlen, self.n_head, self.head_dim)
k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
v = v.view(bsz, seqlen, self.n_local_heads, self.head_dim)
q, k, v = map(lambda x: x.transpose(1, 2), (q, k, v))
if self.kv_cache is not None:
k, v = self.kv_cache.update(input_pos, k, v)
k = k.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)
y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
y = self.wo(y)
return y
class SwiGLU(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.w1 = nn.Linear(config.dim, config.intermediate_size, bias=False)
self.w3 = nn.Linear(config.dim, config.intermediate_size, bias=False)
def forward(self, x: Tensor) -> Tensor:
return F.silu(self.w1(x)) * self.w3(x)
class FeedForward(nn.Module):
def __init__(self, config: ModelArgs) -> None:
super().__init__()
self.swiglu = SwiGLU(config)
self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)
def forward(self, x: Tensor) -> Tensor:
return self.w2(self.swiglu(x))
class RMSNorm(nn.Module):
def __init__(self, dim: int, eps: float = 1e-5):
super().__init__()
self.eps = eps
self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
return x * torch.rsqrt(torch.mean(x * x, dim=-1, keepdim=True) + self.eps)
def forward(self, x: Tensor) -> Tensor:
output = self._norm(x.float()).type_as(x)
return output * self.weight