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* Update TensorRT-LLM --------- Co-authored-by: Eddie-Wang1120 <wangjinheng1120@163.com> Co-authored-by: Shixiaowei02 <39303645+Shixiaowei02@users.noreply.github.com>
40 lines
1.3 KiB
Python
40 lines
1.3 KiB
Python
# Copyright 2024 DeepMind Technologies Limited.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""Base layers."""
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import jax
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import jax.numpy as jnp
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from flax import linen as nn
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class Einsum(nn.Module):
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shape: tuple[int, ...]
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@nn.compact
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def __call__(self, eqn: str, x: jax.Array) -> jax.Array:
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w = self.param('w', nn.initializers.zeros_init(), self.shape)
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return jnp.einsum(eqn, x, w)
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class RMSNorm(nn.Module):
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@nn.compact
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def __call__(self, x):
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scale = self.param('scale', nn.initializers.zeros_init(), (x.shape[-1]))
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var = jnp.mean(jnp.square(x), axis=-1, keepdims=True)
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normed_inputs = jnp.asarray(x * jnp.reciprocal(jnp.sqrt(var + 1e-06)))
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normed_inputs = normed_inputs * (1 + scale)
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return normed_inputs
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