diff --git a/llms/mlx_lm/models/cohere.py b/llms/mlx_lm/models/cohere.py new file mode 100644 index 00000000..724d2007 --- /dev/null +++ b/llms/mlx_lm/models/cohere.py @@ -0,0 +1,171 @@ +from dataclasses import dataclass +from typing import Optional, Tuple + +import mlx.core as mx +import mlx.nn as nn + +from .base import BaseModelArgs +from .layers import LayerNorm + + +@dataclass +class ModelArgs(BaseModelArgs): + model_type: str + hidden_size: int = 8192 + num_hidden_layers: int = 40 + intermediate_size: int = 22528 + num_attention_heads: int = 64 + num_key_value_heads: int = 64 + rope_theta: float = 8000000.0 + vocab_size: int = 256000 + layer_norm_eps: float = 1e-05 + logit_scale: float = 0.0625 + attention_bias: bool = False + layer_norm_bias: bool = False + + +class Attention(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.args = args + + dim = args.hidden_size + self.n_heads = n_heads = args.num_attention_heads + self.n_kv_heads = n_kv_heads = args.num_key_value_heads + + head_dim = args.hidden_size // args.num_attention_heads + self.scale = head_dim**-0.5 + + attetion_bias = args.attention_bias + + self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attetion_bias) + self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attetion_bias) + self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attetion_bias) + self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attetion_bias) + + self.rope = nn.RoPE(head_dim, traditional=True, base=args.rope_theta) + + 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 + + queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) + + # 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: + key_cache, value_cache = cache + queries = self.rope(queries, offset=key_cache.shape[2]) + keys = self.rope(keys, offset=key_cache.shape[2]) + keys = mx.concatenate([key_cache, keys], axis=2) + values = mx.concatenate([value_cache, values], axis=2) + else: + queries = self.rope(queries) + keys = self.rope(keys) + + 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.o_proj(output), (keys, values) + + +class MLP(nn.Module): + def __init__(self, dim, hidden_dim): + super().__init__() + self.gate_proj = nn.Linear(dim, hidden_dim, bias=False) + self.up_proj = nn.Linear(dim, hidden_dim, bias=False) + self.down_proj = nn.Linear(hidden_dim, dim, bias=False) + + def __call__(self, x): + return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x)) + + +class TransformerBlock(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.hidden_size = args.hidden_size + self.n_heads = args.num_attention_heads + + self.self_attn = Attention(args) + self.mlp = MLP(args.hidden_size, args.intermediate_size) + self.input_layernorm = LayerNorm( + args.hidden_size, eps=args.layer_norm_eps, bias=args.layer_norm_bias + ) + self.args = args + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Tuple[mx.array, mx.array]] = None, + ) -> mx.array: + h = self.input_layernorm(x) + attn_h, cache = self.self_attn(h, mask, cache) + ff_h = self.mlp(h) + return attn_h + ff_h + x, cache + + +class CohereModel(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.args = args + self.vocab_size = args.vocab_size + self.num_hidden_layers = args.num_hidden_layers + assert self.vocab_size > 0 + self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) + self.layers = [ + TransformerBlock(args=args) for _ in range(args.num_hidden_layers) + ] + self.norm = LayerNorm( + args.hidden_size, eps=args.layer_norm_eps, bias=args.layer_norm_bias + ) + + def __call__( + self, + inputs: mx.array, + cache=None, + ): + h = self.embed_tokens(inputs) + + mask = None + if h.shape[1] > 1: + mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1]) + mask = mask.astype(h.dtype) + + if cache is None: + cache = [None] * len(self.layers) + + for e, layer in enumerate(self.layers): + h, cache[e] = layer(h, mask, cache[e]) + + return self.norm(h), cache + + +class Model(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.model_type = args.model_type + self.model = CohereModel(args) + + def __call__( + self, + inputs: mx.array, + cache=None, + ): + out, cache = self.model(inputs, cache) + out = out @ self.model.embed_tokens.weight.T + out = out * self.model.args.logit_scale + return out, cache + + @property + def layers(self): + return self.model.layers diff --git a/llms/mlx_lm/models/layers.py b/llms/mlx_lm/models/layers.py index 77d9831f..cf91ad19 100644 --- a/llms/mlx_lm/models/layers.py +++ b/llms/mlx_lm/models/layers.py @@ -23,29 +23,58 @@ class RMSNorm(nn.Module): @partial(mx.compile, shapeless=True) def ln_norm(x, eps, weight=None, bias=None): + """ + Layer normalization for input tensor x. + + Args: + x (np.ndarray): Input tensor. + eps (float, optional): Small value to avoid division by zero. + weight (np.ndarray, optional): Weight tensor for normalization. + bias (np.ndarray, optional): Bias tensor for normalization. + + Returns: + np.ndarray: Normalized tensor. + """ t = x.dtype x = x.astype(mx.float32) + + # Compute mean and variance along the last dimension means = mx.mean(x, axis=-1, keepdims=True) var = mx.var(x, axis=-1, keepdims=True) + + # Normalize the input tensor x = (x - means) * mx.rsqrt(var + eps) x = x.astype(t) - return weight * x + bias if weight is not None else x + + # Apply weight and bias if provided + if weight is not None: + x = x * weight + if bias is not None: + x = x + bias + return x class LayerNorm(nn.Module): - def __init__(self, dims: int, eps: float = 1e-5, affine: bool = True): + def __init__( + self, dims: int, eps: float = 1e-5, affine: bool = True, bias: bool = True + ): super().__init__() - if affine: - self.bias = mx.zeros((dims,)) - self.weight = mx.ones((dims,)) self.eps = eps self.dims = dims + self.affine = affine + + if affine: + self.weight = mx.ones((dims,)) + self.bias = mx.zeros((dims,)) if bias else None def _extra_repr(self): return f"{self.dims}, eps={self.eps}, affine={'weight' in self}" def __call__(self, x: mx.array) -> mx.array: - if "weight" in self: - return ln_norm(x, self.eps, self.weight, self.bias) + if self.affine: + if self.bias is not None: + return ln_norm(x, self.eps, self.weight, self.bias) + else: + return ln_norm(x, self.eps, self.weight) else: return ln_norm(x, self.eps) diff --git a/llms/tests/test_models.py b/llms/tests/test_models.py index 7b99d065..fbe1bfeb 100644 --- a/llms/tests/test_models.py +++ b/llms/tests/test_models.py @@ -254,7 +254,6 @@ class TestModels(unittest.TestCase): self.assertEqual(sanitized_weights["lm_head.weight"], "some_value") def test_starcoder2_tie_word_embeddings_with_lm_head_weight(self): - from mlx_lm.models import starcoder2 args = starcoder2.ModelArgs( @@ -276,6 +275,17 @@ class TestModels(unittest.TestCase): self.assertIn("lm_head.weight", sanitized_weights) self.assertEqual(sanitized_weights["lm_head.weight"], "existing_value") + def test_cohere(self): + from mlx_lm.models import cohere + + args = cohere.ModelArgs( + model_type="cohere", + ) + model = cohere.Model(args) + self.model_test_runner( + model, args.model_type, args.vocab_size, args.num_hidden_layers + ) + if __name__ == "__main__": unittest.main()