deepseek v3 model with pipeline parallelism (#1191)

* deepseekv3

* use upload_large_file instead of deprecated multi comit

* add pipeline generation and example

* comment

* get fp16 working

* use mlx==0.22

llms/mlx_lm/_version.py

@@ -1,3 +1,3 @@
 # Copyright © 2023-2024 Apple Inc.
 
-__version__ = "0.20.4"
+__version__ = "0.21.0"

llms/mlx_lm/examples/pipeline_generate.py

@@ -0,0 +1,75 @@
+# Copyright © 2024 Apple Inc.
+
+"""
+Run with:
+
+```
+/path/to/mpirun \
+ -np 2 \
+ --hostfile /path/to/hosts.txt \
+ python /path/to/pipeline_generate.py --prompt "hello world"
+```
+
+Make sure you can run MLX over MPI on two hosts. For more information see the
+documentation:
+
+https://ml-explore.github.io/mlx/build/html/usage/distributed.html).
+"""
+
+import argparse
+
+import mlx.core as mx
+from mlx_lm import load, stream_generate
+
+parser = argparse.ArgumentParser(description="LLM pipelined inference example")
+parser.add_argument(
+    "--prompt",
+    "-p",
+    default="Write a quicksort in C++.",
+    help="Message to be processed by the model ('-' reads from stdin)",
+)
+parser.add_argument(
+    "--max-tokens",
+    "-m",
+    type=int,
+    default=256,
+    help="Maximum number of tokens to generate",
+)
+args = parser.parse_args()
+
+model_repo = "mlx-community/DeepSeek-V3-3bit"
+
+model, tokenizer = load(model_repo, lazy=True)
+
+messages = [{"role": "user", "content": args.prompt}]
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+
+group = mx.distributed.init()
+rank = group.rank()
+model.model.pipeline(group)
+mx.eval(model.parameters())
+
+# Synchronize processes before generation to avoid timeout if downloading
+# model for the first time.
+mx.eval(mx.distributed.all_sum(mx.array(1.0), stream=mx.cpu))
+
+
+def rprint(*args, **kwargs):
+    if rank == 0:
+        print(*args, **kwargs)
+
+
+for response in stream_generate(model, tokenizer, prompt, max_tokens=args.max_tokens):
+    rprint(response.text, end="", flush=True)
+
+rprint()
+rprint("=" * 10)
+rprint(
+    f"Prompt: {response.prompt_tokens} tokens, "
+    f"{response.prompt_tps:.3f} tokens-per-sec"
+)
+rprint(
+    f"Generation: {response.generation_tokens} tokens, "
+    f"{response.generation_tps:.3f} tokens-per-sec"
+)
+rprint(f"Peak memory: {response.peak_memory:.3f} GB")

llms/mlx_lm/models/deepseek_v3.py

@@ -0,0 +1,460 @@
+# Copyright © 2024 Apple Inc.
+
+import math
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .switch_layers import SwitchGLU
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str = "deepseek_v3"
+    vocab_size: int = 102400
+    hidden_size: int = 4096
+    intermediate_size: int = 11008
+    moe_intermediate_size: int = 1407
+    num_hidden_layers: int = 30
+    num_attention_heads: int = 32
+    num_key_value_heads: int = 32
+    n_shared_experts: Optional[int] = None
+    n_routed_experts: Optional[int] = None
+    routed_scaling_factor: float = 1.0
+    kv_lora_rank: int = 512
+    q_lora_rank: int = 1536
+    qk_rope_head_dim: int = 64
+    v_head_dim: int = 128
+    qk_nope_head_dim: int = 128
+    topk_method: str = "noaux_tc"
+    scoring_func: str = "sigmoid"
+    norm_topk_prob: bool = True
+    n_group: Optional[int] = None
+    topk_group: Optional[int] = None
+    num_experts_per_tok: Optional[int] = None
+    moe_layer_freq: int = 1
+    first_k_dense_replace: int = 0
+    max_position_embeddings: int = 2048
+    rms_norm_eps: float = 1e-6
+    rope_theta: float = 10000.0
+    rope_scaling: Dict = None
+    attention_bias: bool = False
+
+
+def yarn_find_correction_dim(
+    num_rotations, dim, base=10000, max_position_embeddings=2048
+):
+    return (dim * math.log(max_position_embeddings / (num_rotations * 2 * math.pi))) / (
+        2 * math.log(base)
+    )
+
+
+def yarn_find_correction_range(
+    low_rot, high_rot, dim, base=10000, max_position_embeddings=2048
+):
+    low = math.floor(
+        yarn_find_correction_dim(low_rot, dim, base, max_position_embeddings)
+    )
+    high = math.ceil(
+        yarn_find_correction_dim(high_rot, dim, base, max_position_embeddings)
+    )
+    return max(low, 0), min(high, dim - 1)
+
+
+def yarn_get_mscale(scale=1, mscale=1):
+    if scale <= 1:
+        return 1.0
+    return 0.1 * mscale * math.log(scale) + 1.0
+
+
+def yarn_linear_ramp_mask(min_val, max_val, dim):
+    if min_val == max_val:
+        max_val += 0.001  # Prevent singularity
+
+    linear_func = (mx.arange(dim, dtype=mx.float32) - min_val) / (max_val - min_val)
+    return mx.clip(linear_func, 0, 1)
+
+
+class DeepseekV3YarnRotaryEmbedding(nn.Module):
+    def __init__(
+        self,
+        dim,
+        max_position_embeddings=2048,
+        base=10000,
+        scaling_factor=1.0,
+        original_max_position_embeddings=4096,
+        beta_fast=32,
+        beta_slow=1,
+        mscale=1,
+        mscale_all_dim=0,
+    ):
+        super().__init__()
+        self.mscale = yarn_get_mscale(scaling_factor, mscale) / yarn_get_mscale(
+            scaling_factor, mscale_all_dim
+        )
+        freq_extra = base ** (mx.arange(0, dim, 2, dtype=mx.float32) / dim)
+        freq_inter = scaling_factor * base ** (
+            mx.arange(0, dim, 2, dtype=mx.float32) / dim
+        )
+        low, high = yarn_find_correction_range(
+            beta_fast,
+            beta_slow,
+            dim,
+            base,
+            original_max_position_embeddings,
+        )
+        freq_mask = 1.0 - yarn_linear_ramp_mask(low, high, dim // 2)
+        self._freqs = (freq_inter * freq_extra) / (
+            freq_inter * freq_mask + freq_extra * (1 - freq_mask)
+        )
+
+    def __call__(self, x, offset=0):
+        if self.mscale != 1.0:
+            x = self.mscale * x
+        return mx.fast.rope(
+            x,
+            x.shape[-1],
+            traditional=True,
+            base=None,
+            scale=1.0,
+            offset=offset,
+            freqs=self._freqs,
+        )
+
+
+class DeepseekV3Attention(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.q_lora_rank = config.q_lora_rank
+        self.qk_rope_head_dim = config.qk_rope_head_dim
+        self.kv_lora_rank = config.kv_lora_rank
+        self.v_head_dim = config.v_head_dim
+        self.qk_nope_head_dim = config.qk_nope_head_dim
+        self.q_head_dim = config.qk_nope_head_dim + config.qk_rope_head_dim
+
+        self.scale = self.q_head_dim**-0.5
+
+        if self.q_lora_rank is None:
+            self.q_proj = nn.Linear(
+                self.hidden_size, self.num_heads * self.q_head_dim, bias=False
+            )
+        else:
+            self.q_a_proj = nn.Linear(
+                self.hidden_size, self.q_lora_rank, bias=config.attention_bias
+            )
+            self.q_a_layernorm = nn.RMSNorm(self.q_lora_rank)
+            self.q_b_proj = nn.Linear(
+                self.q_lora_rank, self.num_heads * self.q_head_dim, bias=False
+            )
+
+        self.kv_a_proj_with_mqa = nn.Linear(
+            self.hidden_size,
+            self.kv_lora_rank + self.qk_rope_head_dim,
+            bias=config.attention_bias,
+        )
+        self.kv_a_layernorm = nn.RMSNorm(self.kv_lora_rank)
+        self.kv_b_proj = nn.Linear(
+            self.kv_lora_rank,
+            self.num_heads
+            * (self.q_head_dim - self.qk_rope_head_dim + self.v_head_dim),
+            bias=False,
+        )
+
+        self.o_proj = nn.Linear(
+            self.num_heads * self.v_head_dim,
+            self.hidden_size,
+            bias=config.attention_bias,
+        )
+
+        mscale_all_dim = self.config.rope_scaling.get("mscale_all_dim", 0)
+        scaling_factor = self.config.rope_scaling["factor"]
+        if mscale_all_dim:
+            mscale = yarn_get_mscale(scaling_factor, mscale_all_dim)
+            self.scale = self.scale * mscale * mscale
+
+        rope_kwargs = {
+            key: self.config.rope_scaling[key]
+            for key in [
+                "original_max_position_embeddings",
+                "beta_fast",
+                "beta_slow",
+                "mscale",
+                "mscale_all_dim",
+            ]
+            if key in self.config.rope_scaling
+        }
+        self.rope = DeepseekV3YarnRotaryEmbedding(
+            dim=self.qk_rope_head_dim,
+            max_position_embeddings=self.max_position_embeddings,
+            scaling_factor=scaling_factor,
+            base=self.rope_theta,
+            **rope_kwargs,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        B, L, D = x.shape
+
+        if self.q_lora_rank is None:
+            q = self.q_proj(x)
+        else:
+            q = self.q_b_proj(self.q_a_layernorm(self.q_a_proj(x)))
+
+        q = q.reshape(B, L, self.num_heads, self.q_head_dim).transpose(0, 2, 1, 3)
+        q_nope, q_pe = mx.split(q, [self.qk_nope_head_dim], axis=-1)
+        compressed_kv = self.kv_a_proj_with_mqa(x)
+        compressed_kv, k_pe = mx.split(compressed_kv, [self.kv_lora_rank], axis=-1)
+        k_pe = k_pe.reshape(B, L, 1, self.qk_rope_head_dim).transpose(0, 2, 1, 3)
+        kv = self.kv_b_proj(self.kv_a_layernorm(compressed_kv))
+        kv = kv.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3)
+
+        k_nope, values = mx.split(kv, [self.qk_nope_head_dim], axis=-1)
+
+        if cache is not None:
+            q_pe = self.rope(q_pe, cache.offset)
+            k_pe = self.rope(k_pe, cache.offset)
+            k_pe = mx.repeat(k_pe, self.num_heads, axis=1)
+            keys, values = cache.update_and_fetch(
+                mx.concatenate([k_nope, k_pe], axis=-1), values
+            )
+        else:
+            q_pe = self.rope(q_pe)
+            k_pe = self.rope(k_pe)
+            k_pe = mx.repeat(k_pe, self.num_heads, axis=1)
+            keys = mx.concatenate([k_nope, k_pe], axis=-1)
+
+        queries = mx.concatenate([q_nope, q_pe], axis=-1)
+
+        output = scaled_dot_product_attention(
+            queries, keys, values, cache=cache, scale=self.scale, mask=mask
+        )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output)
+
+
+class DeepseekV3MLP(nn.Module):
+    def __init__(
+        self, config: ModelArgs, hidden_size: int = None, intermediate_size: int = None
+    ):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size if hidden_size is None else hidden_size
+        self.intermediate_size = (
+            config.intermediate_size if intermediate_size is None else intermediate_size
+        )
+
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+
+    def __call__(self, x):
+        down_proj = self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+        return down_proj
+
+
+class MoEGate(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.top_k = config.num_experts_per_tok
+        self.norm_topk_prob = config.norm_topk_prob
+        self.n_routed_experts = config.n_routed_experts
+        self.routed_scaling_factor = config.routed_scaling_factor
+        self.topk_method = config.topk_method
+        self.n_group = config.n_group
+        self.topk_group = config.topk_group
+        self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
+        self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))
+
+    def __call__(self, x):
+        gates = x @ self.weight.T
+
+        scores = mx.sigmoid(gates.astype(mx.float32))
+
+        assert self.topk_method == "noaux_tc", "Unsupported topk method."
+        bsz, seq_len = x.shape[:2]
+        scores = scores + self.e_score_correction_bias
+        scores = scores.reshape(bsz, seq_len, self.n_group, -1)
+        group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1)
+        k = self.n_group - self.topk_group
+        group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-1)[..., :k]
+        batch_idx = mx.expand_dims(mx.arange(bsz), (1, 2))
+        seq_idx = mx.expand_dims(mx.arange(seq_len), (0, 2))
+        scores[batch_idx, seq_idx, group_idx] = 0.0
+        scores = scores.reshape(bsz, seq_len, -1)
+
+        k = self.top_k
+        inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
+        scores = mx.take_along_axis(scores, inds, axis=-1)
+        if self.top_k > 1 and self.norm_topk_prob:
+            denominator = scores.sum(axis=-1, keepdims=True) + 1e-20
+            scores = scores / denominator
+        scores = scores * self.routed_scaling_factor
+
+        return inds, scores
+
+
+class DeepseekV3MoE(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.config = config
+        self.num_experts_per_tok = config.num_experts_per_tok
+        self.switch_mlp = SwitchGLU(
+            config.hidden_size, config.moe_intermediate_size, config.n_routed_experts
+        )
+
+        self.gate = MoEGate(config)
+        if config.n_shared_experts is not None:
+            intermediate_size = config.moe_intermediate_size * config.n_shared_experts
+            self.shared_experts = DeepseekV3MLP(
+                config=config, intermediate_size=intermediate_size
+            )
+
+    def __call__(self, x):
+        inds, scores = self.gate(x)
+        y = self.switch_mlp(x, inds)
+        y = (y * scores[..., None]).sum(axis=-2).astype(y.dtype)
+        if self.config.n_shared_experts is not None:
+            y = y + self.shared_experts(x)
+
+        return y
+
+
+class DeepseekV3DecoderLayer(nn.Module):
+    def __init__(self, config: ModelArgs, layer_idx: int):
+        super().__init__()
+        self.self_attn = DeepseekV3Attention(config)
+        self.mlp = (
+            DeepseekV3MoE(config)
+            if (
+                config.n_routed_experts is not None
+                and layer_idx >= config.first_k_dense_replace
+                and layer_idx % config.moe_layer_freq == 0
+            )
+            else DeepseekV3MLP(config)
+        )
+        self.input_layernorm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            config.hidden_size, eps=config.rms_norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        r = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        # Protect against overflow for fp16
+        if out.dtype == mx.float16:
+            out = mx.clip(out, a_min=None, a_max=mx.finfo(mx.float16).max - 1000)
+        return out
+
+
+class DeepseekV3Model(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.vocab_size = config.vocab_size
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = [
+            DeepseekV3DecoderLayer(config, idx)
+            for idx in range(config.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.pipeline_rank = 0
+        self.pipeline_size = 1
+
+    def pipeline(self, group):
+        # Split layers in reverse so rank=0 gets the last layers and
+        # rank=pipeline_size-1 gets the first
+        self.pipeline_rank = group.rank()
+        self.pipeline_size = group.size()
+        layers_per_rank = (
+            len(self.layers) + self.pipeline_size - 1
+        ) // self.pipeline_size
+        start = (self.pipeline_size - self.pipeline_rank - 1) * layers_per_rank
+        self.layers = self.layers[start : start + layers_per_rank]
+
+    def __call__(
+        self,
+        x: mx.array,
+        cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
+    ) -> mx.array:
+        h = self.embed_tokens(x)
+
+        pipeline_rank = self.pipeline_rank
+        pipeline_size = self.pipeline_size
+        if mask is None:
+            mask = create_attention_mask(h, cache)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        # Receive from the previous process in the pipeline
+        if pipeline_rank < pipeline_size - 1:
+            h = mx.distributed.recv_like(h, (pipeline_rank + 1))
+
+        for layer, c in zip(self.layers, cache):
+            h = layer(h, mask, c)
+
+        # Send to the next process in the pipeline
+        if pipeline_rank != 0:
+            h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size)
+
+        # Broadcast h while keeping it in the graph
+        h = mx.distributed.all_gather(h)[: h.shape[0]]
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, config: ModelArgs):
+        super().__init__()
+        self.args = config
+        self.model_type = config.model_type
+        self.model = DeepseekV3Model(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache: Optional[Any] = None,
+        mask: Optional[mx.array] = None,
+    ):
+        out = self.model(inputs, cache, mask)
+        return self.lm_head(out)
+
+    def sanitize(self, weights):
+        for l in range(self.args.num_hidden_layers):
+            prefix = f"model.layers.{l}"
+            for n, m in [("w1", "gate_proj"), ("w2", "down_proj"), ("w3", "up_proj")]:
+                for k in ["weight", "scales", "biases"]:
+                    if f"{prefix}.mlp.experts.0.{m}.{k}" in weights:
+                        to_join = [
+                            weights.pop(f"{prefix}.mlp.experts.{e}.{m}.{k}")
+                            for e in range(self.args.n_routed_experts)
+                        ]
+                        weights[f"{prefix}.mlp.switch_mlp.{m}.{k}"] = mx.stack(to_join)
+
+        # Remove multi-token prediction layer
+        return {k: v for k, v in weights.items() if not k.startswith("model.layers.61")}
+
+    @property
+    def layers(self):
+        return self.model.layers

llms/mlx_lm/requirements.txt

@@ -1,4 +1,4 @@
-mlx>=0.19.2
+mlx>=0.22.0
 numpy
 transformers[sentencepiece]>=4.39.3
 protobuf

llms/mlx_lm/utils.py

@@ -561,7 +561,7 @@ def load(
             Defaults to an empty dictionary.
         adapter_path (str, optional): Path to the LoRA adapters. If provided, applies LoRA layers
             to the model. Default: ``None``.
-        lazy (bool): If False eval the model parameters to make sure they are
+        lazy (bool): If ``False`` eval the model parameters to make sure they are
             loaded in memory before returning, otherwise they will be loaded
             when needed. Default: ``False``
     Returns:
@@ -655,7 +655,7 @@ def upload_to_hub(path: str, upload_repo: str, hf_path: str):
 
         model, tokenizer = load("{upload_repo}")
 
-        prompt="hello"
+        prompt = "hello"
 
         if tokenizer.chat_template is not None:
             messages = [{{"role": "user", "content": prompt}}]

llms/tests/test_models.py

@@ -682,6 +682,43 @@ class TestModels(unittest.TestCase):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
+    def test_deepseek_v3(self):
+        from mlx_lm.models import deepseek_v3
+
+        args = deepseek_v3.ModelArgs(
+            model_type="deepseek_v3",
+            vocab_size=1024,
+            hidden_size=128,
+            intermediate_size=256,
+            moe_intermediate_size=256,
+            num_hidden_layers=4,
+            num_attention_heads=4,
+            num_key_value_heads=2,
+            n_routed_experts=4,
+            n_group=2,
+            topk_group=1,
+            num_experts_per_tok=2,
+            n_shared_experts=1,
+            kv_lora_rank=4,
+            q_lora_rank=4,
+            qk_rope_head_dim=32,
+            v_head_dim=16,
+            qk_nope_head_dim=32,
+            rope_scaling={
+                "beta_fast": 32,
+                "beta_slow": 1,
+                "factor": 40,
+                "mscale": 1.0,
+                "mscale_all_dim": 1.0,
+                "original_max_position_embeddings": 4096,
+                "type": "yarn",
+            },
+        )
+        model = deepseek_v3.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
     def test_gemma2(self):
         from mlx_lm.models import gemma2
 

llms/tests/test_utils_load_model.py

@@ -17,7 +17,7 @@ class TestLoadModelCustomGetClasses(unittest.TestCase):
                 self.config = args
                 self.custom_attribute = "This is a custom model"
 
-            def load_weights(self, weights):
+            def load_weights(self, weights, **kwargs):
                 self.qwenWeights = weights
 
         class CustomQwenConfig: