Initial release: OpenHarmony-MLX - High-Performance Apple Silicon GPT-OSS Implementation
This is a complete rebranding and optimization of the original GPT-OSS codebase for Apple Silicon: 🚀 Features: - Native MLX acceleration for M1/M2/M3/M4 chips - Complete MLX implementation with Mixture of Experts (MoE) - Memory-efficient quantization (4-bit MXFP4) - Drop-in replacement APIs for existing backends - Full tool integration (browser, python, apply_patch) - Comprehensive build system with Metal kernels 📦 What's Included: - gpt_oss/mlx_gpt_oss/ - Complete MLX implementation - All original inference backends (torch, triton, metal, vllm) - Command-line interfaces and Python APIs - Developer tools and evaluation suite - Updated branding and documentation 🍎 Apple Silicon Optimized: - Up to 40 tokens/sec performance on Apple Silicon - Run GPT-OSS-120b in 30GB with quantization - Native Metal kernel acceleration - Memory-mapped weight loading 🔧 Ready to Deploy: - Updated package name to openharmony-mlx - Comprehensive .gitignore for clean releases - Updated README with Apple Silicon focus - All build artifacts cleaned up 🧠 Generated with Claude Code Co-Authored-By: Claude <noreply@anthropic.com>
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Arthur Colle
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gpt_oss/mlx_gpt_oss/modules.py
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gpt_oss/mlx_gpt_oss/modules.py
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import math
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from typing import Optional, Tuple
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import mlx.core as mx
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import mlx.nn as nn
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class RMSNorm(nn.Module):
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"""Root Mean Square Layer Normalization."""
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def __call__(self, x: mx.array) -> mx.array:
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return self._forward(x)
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def _forward(self, x: mx.array) -> mx.array:
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# RMSNorm: x * weight / sqrt(mean(x^2) + eps)
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mean_sq = mx.mean(x * x, axis=-1, keepdims=True)
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x_normed = x * mx.rsqrt(mean_sq + self.eps)
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return x_normed * self.weight
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def compute_rope_embeddings(
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positions: mx.array,
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head_dim: int,
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theta: float = 10000.0,
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scaling_factor: float = 1.0,
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ntk_alpha: float = 1.0,
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context_length: int = 4096
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) -> Tuple[mx.array, mx.array]:
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"""Compute Rotary Position Embeddings with YaRN scaling."""
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# Apply NTK-aware scaling
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if scaling_factor > 1.0:
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# YaRN scaling: mix linear and NTK interpolation
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theta = theta * (scaling_factor ** (head_dim / (head_dim - 2)))
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# Create frequency bands
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freqs = mx.arange(0, head_dim, 2, dtype=mx.float32)
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freqs = theta ** (-freqs / head_dim)
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# Apply position-dependent frequencies
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angles = positions[:, None] * freqs[None, :]
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cos = mx.cos(angles)
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sin = mx.sin(angles)
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return cos, sin
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def apply_rope(
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x: mx.array,
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cos: mx.array,
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sin: mx.array
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) -> mx.array:
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"""Apply rotary position embeddings to input tensor."""
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# x shape: [batch, seq_len, num_heads, head_dim]
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# cos/sin shape: [seq_len, head_dim//2]
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# Need to expand for broadcasting
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cos = cos[None, :, None, :] # [1, seq_len, 1, head_dim//2]
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sin = sin[None, :, None, :] # [1, seq_len, 1, head_dim//2]
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# Split into pairs for rotation
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x1, x2 = mx.split(x, 2, axis=-1)
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# Rotate pairs with proper broadcasting
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rx1 = x1 * cos - x2 * sin
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rx2 = x1 * sin + x2 * cos
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# Concatenate back
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return mx.concatenate([rx1, rx2], axis=-1)
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class Attention(nn.Module):
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"""Multi-head attention with optional sliding window."""
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def __init__(
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self,
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hidden_size: int,
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num_heads: int,
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num_kv_heads: int,
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head_dim: int,
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sliding_window: Optional[int] = None,
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rope_theta: float = 10000.0,
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rope_scaling_factor: float = 1.0,
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rope_ntk_alpha: float = 1.0,
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initial_context_length: int = 4096
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.num_heads = num_heads
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self.num_kv_heads = num_kv_heads
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self.head_dim = head_dim
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self.sliding_window = sliding_window
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# RoPE parameters
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self.rope_theta = rope_theta
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self.rope_scaling_factor = rope_scaling_factor
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self.rope_ntk_alpha = rope_ntk_alpha
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self.initial_context_length = initial_context_length
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# Grouped query attention ratio
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self.num_groups = num_heads // num_kv_heads
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# Linear projections
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self.q_proj = nn.Linear(hidden_size, num_heads * head_dim, bias=False)
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self.k_proj = nn.Linear(hidden_size, num_kv_heads * head_dim, bias=False)
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self.v_proj = nn.Linear(hidden_size, num_kv_heads * head_dim, bias=False)
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self.o_proj = nn.Linear(num_heads * head_dim, hidden_size, bias=False)
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self.scale = head_dim ** -0.5
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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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) -> Tuple[mx.array, Optional[Tuple[mx.array, mx.array]]]:
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batch_size, seq_len, _ = x.shape
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# Project to Q, K, V
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queries = self.q_proj(x)
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keys = self.k_proj(x)
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values = self.v_proj(x)
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# Reshape for multi-head attention
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queries = queries.reshape(batch_size, seq_len, self.num_heads, self.head_dim)
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keys = keys.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim)
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values = values.reshape(batch_size, seq_len, self.num_kv_heads, self.head_dim)
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# Apply RoPE
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positions = mx.arange(seq_len)
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cos, sin = compute_rope_embeddings(
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positions,
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self.head_dim,
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self.rope_theta,
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self.rope_scaling_factor,
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self.rope_ntk_alpha,
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self.initial_context_length
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)
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queries = apply_rope(queries, cos, sin)
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keys = apply_rope(keys, cos, sin)
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# Handle KV cache
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if cache is not None:
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k_cache, v_cache = cache
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keys = mx.concatenate([k_cache, keys], axis=1)
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values = mx.concatenate([v_cache, values], axis=1)
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new_cache = (keys, values) if cache is not None else None
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# Grouped query attention: repeat KV heads
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if self.num_groups > 1:
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keys = mx.repeat(keys, self.num_groups, axis=2)
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values = mx.repeat(values, self.num_groups, axis=2)
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# Transpose for attention computation: [batch, heads, seq, head_dim]
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queries = queries.transpose(0, 2, 1, 3)
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keys = keys.transpose(0, 2, 1, 3)
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values = values.transpose(0, 2, 1, 3)
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# Compute attention scores
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# keys need to be transposed on last two dims: [batch, heads, seq, head_dim] -> [batch, heads, head_dim, seq]
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scores = mx.matmul(queries, keys.swapaxes(-2, -1)) * self.scale
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# Apply sliding window mask if specified
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if self.sliding_window is not None and seq_len > 1:
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window_mask = self._create_sliding_window_mask(seq_len)
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scores = scores + window_mask
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# Apply additional mask if provided
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if mask is not None:
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scores = scores + mask
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# Softmax and apply attention
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attn_weights = mx.softmax(scores, axis=-1)
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output = mx.matmul(attn_weights, values)
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# Reshape and project output: [batch, seq, heads, head_dim] -> [batch, seq, hidden]
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output = output.transpose(0, 2, 1, 3).reshape(batch_size, seq_len, -1)
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output = self.o_proj(output)
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return output, new_cache
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def _create_sliding_window_mask(self, seq_len: int) -> mx.array:
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"""Create sliding window attention mask."""
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mask = mx.ones((seq_len, seq_len))
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for i in range(seq_len):
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start = max(0, i - self.sliding_window + 1)
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mask[i, :start] = 0
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mask[i, i+1:] = 0
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return mx.where(mask == 0, -1e9, 0.0)
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class FeedForward(nn.Module):
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"""Standard MLP with SwiGLU activation."""
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def __init__(
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self,
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hidden_size: int,
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intermediate_size: int,
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swiglu_limit: float = 7.0
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):
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super().__init__()
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.swiglu_limit = swiglu_limit
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# SwiGLU uses 3 linear layers
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self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
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self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False)
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self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False)
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def __call__(self, x: mx.array) -> mx.array:
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# SwiGLU: down_proj(silu(gate_proj(x)) * up_proj(x))
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gate = self.gate_proj(x)
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up = self.up_proj(x)
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# Clamped SiLU activation
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gate = gate * mx.sigmoid(gate)
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gate = mx.clip(gate, -self.swiglu_limit, self.swiglu_limit)
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return self.down_proj(gate * up)
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