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Switch to fast RMS/LN Norm (#603)

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* use nn.RMSNorm, use sdpa, cleanup

* bump mlx versions

* minor update

* use fast layer norm

* version bump

* update requirement for whisper

* update requirement for gguf
This commit is contained in:
Awni Hannun
2024-03-23 07:13:51 -07:00
committed by GitHub
parent fbed720d6f
commit b8a348c1b8
44 changed files with 144 additions and 1155 deletions
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29
t5/t5.py
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@@ -134,21 +134,6 @@ class MultiHeadAttention(nn.Module):
return self.out_proj(values_hat), (keys, values)
class RMSNorm(nn.Module):
def __init__(self, dims: int, eps: float = 1e-5):
super().__init__()
self.weight = mx.ones((dims,))
self.eps = eps
def _norm(self, x):
return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
def __call__(self, x):
t = x.dtype
output = self._norm(x).astype(t)
return self.weight * output
class DenseActivation(nn.Module):
def __init__(self, config: T5Config):
super().__init__()
@@ -184,8 +169,8 @@ class TransformerEncoderLayer(nn.Module):
def __init__(self, config: T5Config):
super().__init__()
self.attention = MultiHeadAttention(config)
self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln2 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dense = DenseActivation(config)
def __call__(self, x, mask):
@@ -204,7 +189,7 @@ class TransformerEncoder(nn.Module):
self.layers = [
TransformerEncoderLayer(config) for i in range(config.num_layers)
]
self.ln = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.relative_attention_bias = RelativePositionBias(config, bidirectional=True)
def __call__(self, x: mx.array):
@@ -219,9 +204,9 @@ class TransformerDecoderLayer(nn.Module):
super().__init__()
self.self_attention = MultiHeadAttention(config)
self.cross_attention = MultiHeadAttention(config)
self.ln1 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln2 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln3 = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln1 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln2 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln3 = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.dense = DenseActivation(config)
def __call__(
@@ -252,7 +237,7 @@ class TransformerDecoder(nn.Module):
super().__init__()
n_layers = getattr(config, "num_decoder_layers", config.num_layers)
self.layers = [TransformerDecoderLayer(config) for i in range(n_layers)]
self.ln = RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.ln = nn.RMSNorm(config.d_model, eps=config.layer_norm_epsilon)
self.relative_attention_bias = RelativePositionBias(config, bidirectional=False)
def __call__(self, x, memory, mask, memory_mask, cache=None):