mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 09:21:18 +08:00
Add support for ibm granite (#758)
* add support for granite 3-8B config * add gpt_bigcode * add positional embedding condition. * add support for granite 3-8B config * add gpt_bigcode * add positional embedding condition. * remove unused function * rebase fix * move position emebedding to mask creation * add to tuner and format * add support for granite 3-8B config * add gpt_bigcode * add positional embedding condition. * add support for granite 3-8B config * add gpt_bigcode * add positional embedding condition. * rebase fix * move position emebedding to mask creation * add to tuner and format * refactor mask * remove dropout layers
This commit is contained in:
Prince Canuma
GitHub
parent
9fc6efbd90
commit
b044ce2acf
@ -4,6 +4,13 @@ from dataclasses import dataclass
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import mlx.core as mx
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def create_additive_causal_mask(N: int, offset: int = 0):
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rinds = mx.arange(offset + N)
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linds = mx.arange(offset, offset + N) if offset else rinds
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mask = linds[:, None] < rinds[None]
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return mask * -1e9
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class KVCache:
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def __init__(self, head_dim, n_kv_heads):
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195
llms/mlx_lm/models/gpt_bigcode.py
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195
llms/mlx_lm/models/gpt_bigcode.py
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@ -0,0 +1,195 @@
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from dataclasses import dataclass
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from typing import Dict, Optional, Tuple, Union
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import mlx.core as mx
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import mlx.nn as nn
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import numpy as np
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from .base import BaseModelArgs, create_additive_causal_mask
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@dataclass
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class ModelArgs(BaseModelArgs):
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model_type: str
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n_embd: int
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n_layer: int
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n_inner: int
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n_head: int
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n_positions: int
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layer_norm_epsilon: float
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vocab_size: int
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num_key_value_heads: int = None
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multi_query: bool = True
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attention_bias: bool = True
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mlp_bias: bool = True
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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 = 1 if self.multi_query else self.n_head
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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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self.dim = dim = args.n_embd
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self.n_heads = n_heads = args.n_head
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self.n_kv_heads = n_kv_heads = 1 if args.multi_query else args.n_head
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self.head_dim = head_dim = dim // n_heads
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self.kv_dim = n_kv_heads * head_dim
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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.c_attn = nn.Linear(dim, dim + 2 * self.kv_dim, bias=attention_bias)
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self.c_proj = nn.Linear(dim, dim, bias=attention_bias)
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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[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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B, L, D = x.shape
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qkv = self.c_attn(x)
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queries, keys, values = mx.split(
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qkv, [self.dim, self.dim + self.kv_dim], axis=-1
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)
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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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keys, values = cache.update_and_fetch(keys, values)
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output = mx.fast.scaled_dot_product_attention(
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queries, keys, values, 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.c_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.n_embd
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hidden_dim = args.n_inner
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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.c_fc = nn.Linear(dim, hidden_dim, bias=mlp_bias)
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self.c_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
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def __call__(self, x) -> mx.array:
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return self.c_proj(nn.gelu(self.c_fc(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.n_head = args.n_head
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self.n_embd = args.n_embd
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self.attn = Attention(args)
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self.mlp = MLP(args)
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self.ln_1 = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
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self.ln_2 = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
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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[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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r = self.attn(self.ln_1(x), mask, cache)
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h = x + r
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r = self.mlp(self.ln_2(h))
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out = h + r
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return out
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class GPTBigCodeModel(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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assert self.vocab_size > 0
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self.wte = nn.Embedding(args.vocab_size, args.n_embd)
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self.wpe = nn.Embedding(args.n_positions, args.n_embd)
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self.h = [TransformerBlock(args=args) for _ in range(args.n_layer)]
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self.ln_f = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
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def __call__(
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self,
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inputs: mx.array,
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cache=None,
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):
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B, L = inputs.shape
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hidden_states = self.wte(inputs)
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mask = None
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if hidden_states.shape[1] > 1:
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position_ids = mx.array(np.arange(L))
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hidden_states += self.wpe(position_ids)
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mask = create_additive_causal_mask(
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hidden_states.shape[1], cache[0].offset if cache is not None else 0
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)
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mask = mask.astype(hidden_states.dtype)
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if cache is None:
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cache = [None] * len(self.h)
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for layer, c in zip(self.h, cache):
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hidden_states = layer(hidden_states, mask, cache=c)
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return self.ln_f(hidden_states)
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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.transformer = GPTBigCodeModel(args)
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if not args.tie_word_embeddings:
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self.lm_head = nn.Linear(args.n_embd, 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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cache=None,
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):
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out = self.transformer(inputs, cache)
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if self.args.tie_word_embeddings:
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out = self.transformer.wte.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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@property
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def layers(self):
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return self.transformer.h
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@property
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def head_dim(self):
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return self.args.n_embd // self.args.n_head
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@property
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def n_kv_heads(self):
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return self.args.num_key_value_heads
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@ -4,7 +4,7 @@ from typing import Dict, Optional, Tuple, 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
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from .base import BaseModelArgs, create_additive_causal_mask
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@dataclass
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@ -17,9 +17,12 @@ class ModelArgs(BaseModelArgs):
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rms_norm_eps: float
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vocab_size: int
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num_key_value_heads: 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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@ -44,11 +47,15 @@ class Attention(nn.Module):
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head_dim = 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=False)
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self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
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self.o_proj = nn.Linear(n_heads * head_dim, dim, 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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rope_scale = (
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1 / args.rope_scaling["factor"]
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@ -93,11 +100,19 @@ class Attention(nn.Module):
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class MLP(nn.Module):
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def __init__(self, dim, hidden_dim):
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
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self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
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self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
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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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@ -109,7 +124,7 @@ class TransformerBlock(nn.Module):
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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.hidden_size, args.intermediate_size)
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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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@ -129,13 +144,6 @@ class TransformerBlock(nn.Module):
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return out
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def create_additive_causal_mask(N: int, offset: int = 0):
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rinds = mx.arange(offset + N)
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linds = mx.arange(offset, offset + N) if offset else rinds
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mask = linds[:, None] < rinds[None]
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return mask * -1e9
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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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@ -175,10 +183,11 @@ class LlamaModel(nn.Module):
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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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self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
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self.args = 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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@ -186,7 +195,11 @@ class Model(nn.Module):
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cache=None,
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):
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out = self.model(inputs, cache)
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return self.lm_head(out)
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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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@ -106,6 +106,9 @@ def linear_to_lora_layers(
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if model.model_type == "qwen2_moe":
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keys.add("mlp.gate")
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keys.add("mlp.shared_expert_gate")
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elif model.model_type == "gpt_bigcode":
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keys = set(["attn.c_attn"])
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elif model.model_type == "olmo":
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keys = set(["att_proj"])
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elif model.model_type == "openelm":
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