mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-25 01:41:19 +08:00
236 lines
7.6 KiB
Python
236 lines
7.6 KiB
Python
# Copyright © 2023-2024 Apple Inc.
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from dataclasses import dataclass
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from typing import Any, Dict, Optional, Union
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import mlx.core as mx
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import mlx.nn as nn
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from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
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from .rope_utils import initialize_rope
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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hidden_size: int
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num_hidden_layers: int
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intermediate_size: int
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num_attention_heads: int
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rms_norm_eps: float
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vocab_size: int
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head_dim: Optional[int] = None
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max_position_embeddings: Optional[int] = None
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num_key_value_heads: Optional[int] = None
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attention_bias: bool = False
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mlp_bias: bool = False
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rope_theta: float = 10000
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rope_traditional: bool = False
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rope_scaling: Optional[Dict[str, Union[float, str]]] = None
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tie_word_embeddings: bool = True
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def __post_init__(self):
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if self.num_key_value_heads is None:
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self.num_key_value_heads = self.num_attention_heads
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class Attention(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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dim = args.hidden_size
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self.n_heads = n_heads = args.num_attention_heads
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self.n_kv_heads = n_kv_heads = args.num_key_value_heads
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self.head_dim = head_dim = args.head_dim or args.hidden_size // n_heads
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self.scale = head_dim**-0.5
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if hasattr(args, "attention_bias"):
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attention_bias = args.attention_bias
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else:
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attention_bias = False
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self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
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self.rope = initialize_rope(
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self.head_dim,
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args.rope_theta,
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args.rope_traditional,
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args.rope_scaling,
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args.max_position_embeddings,
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)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Any] = None,
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) -> mx.array:
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B, L, D = x.shape
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queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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if cache is not None:
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queries = self.rope(queries, offset=cache.offset)
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keys = self.rope(keys, offset=cache.offset)
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keys, values = cache.update_and_fetch(keys, values)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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output = scaled_dot_product_attention(
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queries, keys, values, cache=cache, scale=self.scale, mask=mask
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)
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output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output)
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class MLP(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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dim = args.hidden_size
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hidden_dim = args.intermediate_size
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if hasattr(args, "mlp_bias"):
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mlp_bias = args.mlp_bias
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else:
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mlp_bias = False
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self.gate_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
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self.down_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
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self.up_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
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def __call__(self, x) -> mx.array:
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return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
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class TransformerBlock(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.num_attention_heads = args.num_attention_heads
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self.hidden_size = args.hidden_size
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self.self_attn = Attention(args)
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self.mlp = MLP(args)
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self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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self.post_attention_layernorm = nn.RMSNorm(
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args.hidden_size, eps=args.rms_norm_eps
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)
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self.args = args
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Any] = None,
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) -> mx.array:
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r = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out
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class LlamaModel(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.vocab_size = args.vocab_size
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self.num_hidden_layers = args.num_hidden_layers
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assert self.vocab_size > 0
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self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
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self.layers = [
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TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
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]
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self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
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def __call__(
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self,
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inputs: mx.array,
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mask: mx.array = None,
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cache=None,
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):
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h = self.embed_tokens(inputs)
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if mask is None:
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mask = create_attention_mask(h, cache)
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if cache is None:
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cache = [None] * len(self.layers)
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for layer, c in zip(self.layers, cache):
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h = layer(h, mask, cache=c)
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return self.norm(h)
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class Model(nn.Module):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.args = args
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self.model_type = args.model_type
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self.model = LlamaModel(args)
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if not args.tie_word_embeddings:
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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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def __call__(
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self,
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inputs: mx.array,
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mask: mx.array = None,
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cache=None,
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):
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out = self.model(inputs, mask, cache)
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if self.args.tie_word_embeddings:
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out = self.model.embed_tokens.as_linear(out)
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else:
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out = self.lm_head(out)
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return out
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def sanitize(self, weights):
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# Remove unused precomputed rotary freqs
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return {
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k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
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}
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def shard(self, group: Optional[mx.distributed.Group] = None):
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group = group or mx.distributed.init()
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def all_to_sharded(l):
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if isinstance(l, nn.QuantizedLinear):
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return nn.QuantizedAllToShardedLinear.from_quantized_linear(l, group)
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else:
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return nn.AllToShardedLinear.from_linear(l, group)
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def sharded_to_all(l):
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if isinstance(l, nn.QuantizedLinear):
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return nn.QuantizedShardedToAllLinear.from_quantized_linear(l, group)
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else:
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return nn.ShardedToAllLinear.from_linear(l, group)
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N = group.size()
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for layer in self.model.layers:
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# Shard the self attention
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layer.self_attn.q_proj = all_to_sharded(layer.self_attn.q_proj)
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layer.self_attn.k_proj = all_to_sharded(layer.self_attn.k_proj)
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layer.self_attn.v_proj = all_to_sharded(layer.self_attn.v_proj)
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layer.self_attn.o_proj = sharded_to_all(layer.self_attn.o_proj)
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layer.self_attn.n_heads //= N
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layer.self_attn.n_kv_heads //= N
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# Shard the MLP
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layer.mlp.gate_proj = all_to_sharded(layer.mlp.gate_proj)
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layer.mlp.down_proj = sharded_to_all(layer.mlp.down_proj)
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layer.mlp.up_proj = all_to_sharded(layer.mlp.up_proj)
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@property
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def layers(self):
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return self.model.layers
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