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

llms/mlx_lm/models/llama.py

@@ -4,7 +4,7 @@ from typing import Dict, Optional, Tuple, Union
 import mlx.core as mx
 import mlx.nn as nn
 
-from .base import BaseModelArgs
+from .base import BaseModelArgs, create_additive_causal_mask
 
 
 @dataclass
@@ -17,9 +17,12 @@ class ModelArgs(BaseModelArgs):
     rms_norm_eps: float
     vocab_size: int
     num_key_value_heads: int = None
+    attention_bias: bool = False
+    mlp_bias: bool = False
     rope_theta: float = 10000
     rope_traditional: bool = False
     rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = True
 
     def __post_init__(self):
         if self.num_key_value_heads is None:
@@ -44,11 +47,15 @@ class Attention(nn.Module):
 
         head_dim = args.hidden_size // n_heads
         self.scale = head_dim**-0.5
+        if hasattr(args, "attention_bias"):
+            attention_bias = args.attention_bias
+        else:
+            attention_bias = False
 
-        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
-        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
-        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
 
         rope_scale = (
             1 / args.rope_scaling["factor"]
@@ -93,11 +100,19 @@ class Attention(nn.Module):
 
 
 class MLP(nn.Module):
-    def __init__(self, dim, hidden_dim):
+    def __init__(self, args: ModelArgs):
         super().__init__()
-        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
-        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+
+        dim = args.hidden_size
+        hidden_dim = args.intermediate_size
+        if hasattr(args, "mlp_bias"):
+            mlp_bias = args.mlp_bias
+        else:
+            mlp_bias = False
+
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
 
     def __call__(self, x) -> mx.array:
         return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
@@ -109,7 +124,7 @@ class TransformerBlock(nn.Module):
         self.num_attention_heads = args.num_attention_heads
         self.hidden_size = args.hidden_size
         self.self_attn = Attention(args)
-        self.mlp = MLP(args.hidden_size, args.intermediate_size)
+        self.mlp = MLP(args)
         self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
         self.post_attention_layernorm = nn.RMSNorm(
             args.hidden_size, eps=args.rms_norm_eps
@@ -129,13 +144,6 @@ class TransformerBlock(nn.Module):
         return out
 
 
-def create_additive_causal_mask(N: int, offset: int = 0):
-    rinds = mx.arange(offset + N)
-    linds = mx.arange(offset, offset + N) if offset else rinds
-    mask = linds[:, None] < rinds[None]
-    return mask * -1e9
-
-
 class LlamaModel(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
@@ -175,10 +183,11 @@ class LlamaModel(nn.Module):
 class Model(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
+        self.args = args
         self.model_type = args.model_type
         self.model = LlamaModel(args)
-        self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
-        self.args = args
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
 
     def __call__(
         self,
@@ -186,7 +195,11 @@ class Model(nn.Module):
         cache=None,
     ):
         out = self.model(inputs, cache)
-        return self.lm_head(out)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
 
     def sanitize(self, weights):
         # Remove unused precomputed rotary freqs