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/base.py

@@ -4,6 +4,13 @@ from dataclasses import dataclass
 import mlx.core as mx
 
 
+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 KVCache:
 
     def __init__(self, head_dim, n_kv_heads):

llms/mlx_lm/models/gpt_bigcode.py

@@ -0,0 +1,195 @@
+from dataclasses import dataclass
+from typing import Dict, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+import numpy as np
+
+from .base import BaseModelArgs, create_additive_causal_mask
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    n_embd: int
+    n_layer: int
+    n_inner: int
+    n_head: int
+    n_positions: int
+    layer_norm_epsilon: float
+    vocab_size: int
+    num_key_value_heads: int = None
+    multi_query: bool = True
+    attention_bias: bool = True
+    mlp_bias: bool = True
+    tie_word_embeddings: bool = True
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = 1 if self.multi_query else self.n_head
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        self.dim = dim = args.n_embd
+        self.n_heads = n_heads = args.n_head
+        self.n_kv_heads = n_kv_heads = 1 if args.multi_query else args.n_head
+
+        self.head_dim = head_dim = dim // n_heads
+
+        self.kv_dim = n_kv_heads * head_dim
+
+        self.scale = head_dim**-0.5
+
+        if hasattr(args, "attention_bias"):
+            attention_bias = args.attention_bias
+        else:
+            attention_bias = False
+
+        self.c_attn = nn.Linear(dim, dim + 2 * self.kv_dim, bias=attention_bias)
+        self.c_proj = nn.Linear(dim, dim, bias=attention_bias)
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        qkv = self.c_attn(x)
+        queries, keys, values = mx.split(
+            qkv, [self.dim, self.dim + self.kv_dim], axis=-1
+        )
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            keys, values = cache.update_and_fetch(keys, values)
+
+        output = mx.fast.scaled_dot_product_attention(
+            queries, keys, values, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.c_proj(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        dim = args.n_embd
+        hidden_dim = args.n_inner
+        if hasattr(args, "mlp_bias"):
+            mlp_bias = args.mlp_bias
+        else:
+            mlp_bias = False
+
+        self.c_fc = nn.Linear(dim, hidden_dim, bias=mlp_bias)
+        self.c_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
+
+    def __call__(self, x) -> mx.array:
+        return self.c_proj(nn.gelu(self.c_fc(x)))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.n_head = args.n_head
+        self.n_embd = args.n_embd
+        self.attn = Attention(args)
+        self.mlp = MLP(args)
+        self.ln_1 = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
+        self.ln_2 = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        r = self.attn(self.ln_1(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.ln_2(h))
+        out = h + r
+        return out
+
+
+class GPTBigCodeModel(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+        assert self.vocab_size > 0
+        self.wte = nn.Embedding(args.vocab_size, args.n_embd)
+        self.wpe = nn.Embedding(args.n_positions, args.n_embd)
+        self.h = [TransformerBlock(args=args) for _ in range(args.n_layer)]
+        self.ln_f = nn.LayerNorm(args.n_embd, eps=args.layer_norm_epsilon)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        B, L = inputs.shape
+
+        hidden_states = self.wte(inputs)
+
+        mask = None
+        if hidden_states.shape[1] > 1:
+
+            position_ids = mx.array(np.arange(L))
+            hidden_states += self.wpe(position_ids)
+
+            mask = create_additive_causal_mask(
+                hidden_states.shape[1], cache[0].offset if cache is not None else 0
+            )
+            mask = mask.astype(hidden_states.dtype)
+
+        if cache is None:
+            cache = [None] * len(self.h)
+
+        for layer, c in zip(self.h, cache):
+            hidden_states = layer(hidden_states, mask, cache=c)
+
+        return self.ln_f(hidden_states)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.transformer = GPTBigCodeModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.n_embd, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.transformer(inputs, cache)
+        if self.args.tie_word_embeddings:
+            out = self.transformer.wte.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    @property
+    def layers(self):
+        return self.transformer.h
+
+    @property
+    def head_dim(self):
+        return self.args.n_embd // self.args.n_head
+
+    @property
+    def n_kv_heads(self):
+        return self.args.num_key_value_heads

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.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
-        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        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=False)
+        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=False)
+        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)
+        dim = args.hidden_size
-        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+        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)
+        if not args.tie_word_embeddings:
-        self.args = args
+            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

llms/mlx_lm/tuner/utils.py

@@ -106,6 +106,9 @@ def linear_to_lora_layers(
         if model.model_type == "qwen2_moe":
             keys.add("mlp.gate")
             keys.add("mlp.shared_expert_gate")
+
+    elif model.model_type == "gpt_bigcode":
+        keys = set(["attn.c_attn"])
     elif model.model_type == "olmo":
         keys = set(["att_proj"])
     elif model.model_type == "openelm":