DeepSeek V4 Official Launch Deep Dive: 1.6T MoE + Peak-Valley Pricing + DSpark Inference Acceleration

"""
DeepSeek V4 1M Context Attention Mechanism (Simplified)
Core: ALiBi Positional Encoding + Sliding Window + Sparse Attention
"""

import math
import torch
import torch.nn as nn
import torch.nn.functional as F

class DeepSeekLongContextAttention(nn.Module):
    """Hierarchical attention supporting 1M context"""
    
    def __init__(self, d_model=7168, n_heads=64, window_size=8192):
        super().__init__()
        self.d_model = d_model
        self.n_heads = n_heads
        self.head_dim = d_model // n_heads
        self.window_size = window_size
        
        self.wq = nn.Linear(d_model, d_model, bias=False)
        self.wk = nn.Linear(d_model, d_model, bias=False)
        self.wv = nn.Linear(d_model, d_model, bias=False)
        self.wo = nn.Linear(d_model, d_model, bias=False)
        
        self.register_buffer(
            "alibi_slopes",
            self._compute_alibi_slopes(n_heads)
        )
    
    def _compute_alibi_slopes(self, n_heads):
        """Compute ALiBi slopes for 1M context support"""
        def get_slopes(n):
            def _get_slopes_power_of_2(n):
                start = 2 ** (-8 / n)
                return [start ** i for i in range(n)]
            
            if math.log2(n).is_integer():
                return _get_slopes_power_of_2(n)
            else:
                closest_power = 2 ** math.floor(math.log2(n))
                slopes = _get_slopes_power_of_2(closest_power)
                extra = n - closest_power
                extra_slopes = _get_slopes_power_of_2(2 * closest_power)
                return slopes + extra_slopes[0:extra:2]
        
        return torch.tensor(get_slopes(n_heads))
    
    def forward(self, x, layer_idx=0):
        batch, seq_len, _ = x.shape
        
        Q = self.wq(x).view(batch, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        K = self.wk(x).view(batch, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        V = self.wv(x).view(batch, seq_len, self.n_heads, self.head_dim).transpose(1, 2)
        
        effective_window = min(self.window_size, max(4096, self.window_size - layer_idx * 512))
        chunk_size = 16384
        outputs = []
        
        for i in range(0, seq_len, chunk_size):
            chunk_start = max(0, i - effective_window)
            chunk_end = min(seq_len, i + chunk_size)
            
            Q_chunk = Q[:, :, i:min(i+chunk_size, seq_len), :]
            K_chunk = K[:, :, chunk_start:chunk_end, :]
            V_chunk = V[:, :, chunk_start:chunk_end, :]
            
            scores = torch.matmul(Q_chunk, K_chunk.transpose(-2, -1)) / math.sqrt(self.head_dim)
            
            pos_diff = torch.arange(
                chunk_start - i, chunk_end - i, 
                device=x.device
            ).unsqueeze(0).unsqueeze(0)
            alibi_bias = -self.alibi_slopes.view(-1, 1, 1) * pos_diff.abs()
            scores = scores + alibi_bias[:, :, :, :scores.size(-1)]
            
            if seq_len > effective_window:
                causal_mask = torch.triu(
                    torch.ones(scores.size(-2), scores.size(-1), device=x.device),
                    diagonal=1
                ) * -1e10
                scores = scores + causal_mask
            
            attn_weights = F.softmax(scores, dim=-1)
            chunk_output = torch.matmul(attn_weights, V_chunk)
            outputs.append(chunk_output)
        
        output = torch.cat(outputs, dim=2)
        output = output.transpose(1, 2).contiguous().view(batch, seq_len, self.d_model)
        return self.wo(output)


def estimate_memory_for_1m_context():
    """Estimate memory usage for 1M context"""
    d_model = 7168
    n_heads = 64
    head_dim = d_model // n_heads
    seq_len = 1_000_000
    
    full_attn_memory = (seq_len ** 2 * n_heads * 2) / (1024 ** 3)
    print(f"Full Attention Score Matrix: {full_attn_memory:.1f} GB")
    
    window_size = 8192
    window_attn_memory = (seq_len * window_size * n_heads * 2) / (1024 ** 3)
    print(f"Sliding Window Attention Score Matrix: {window_attn_memory:.1f} GB")
    
    kv_cache = (seq_len * d_model * 2 * 2) / (1024 ** 3)
    print(f"KV Cache (16bit): {kv_cache:.1f} GB")
    
    print(f"DeepSeek V4 Actual Peak: ≈80 GB (H100-80GB)")

estimate_memory_for_1m_context()

// SWE-bench Result Analysis Tool
package main

import (
    "fmt"
    "sort"
)

type SWEBenchResult struct {
    ModelName   string
    TotalIssues int
    Resolved    int
    PassRate    float64
    AvgTimeCost float64
}

type CostEfficiency struct {
    Model    string
    PerfCost float64
}

func analyzeSWEBench(results []SWEBenchResult) {
    sort.Slice(results, func(i, j int) bool {
        return results[i].PassRate > results[j].PassRate
    })

    fmt.Println("=== SWE-bench Verified Rankings ===")
    for i, r := range results {
        fmt.Printf("#%d: %s | Resolve Rate: %.1f%% | Avg Time: %.0fs\n",
            i+1, r.ModelName, r.PassRate*100, r.AvgTimeCost)
    }

    fmt.Println("\n=== Cost Efficiency Analysis ===")
    costData := []CostEfficiency{
        {"DeepSeek V4 Pro", 0.15},
        {"GPT-5.6 Terra", 2.08},
        {"Claude Fable 5", 6.21},
    }
    
    for _, c := range costData {
        fmt.Printf("%s: Cost per 1%% resolve rate $%.2f\n", c.Model, c.PerfCost)
    }
}

func main() {
    results := []SWEBenchResult{
        {ModelName: "DeepSeek V4 Pro", TotalIssues: 500, Resolved: 403, PassRate: 0.806, AvgTimeCost: 89.5},
        {ModelName: "GPT-5.6 Terra", TotalIssues: 500, Resolved: 390, PassRate: 0.781, AvgTimeCost: 67.2},
        {ModelName: "Claude Fable 5", TotalIssues: 500, Resolved: 401, PassRate: 0.803, AvgTimeCost: 145.8},
    }
    analyzeSWEBench(results)
}

"""
DeepSeek V4 Intelligent Scheduling Client
Implements time-aware API calling with optimal model/period selection
"""

import datetime
from dataclasses import dataclass
from typing import Optional, Callable
import requests

@dataclass
class DeepSeekPricing:
    model: str
    peak_input: float
    peak_output: float
    offpeak_input: float
    offpeak_output: float
    night_input: float
    night_output: float

class DeepSeekScheduler:
    """Intelligent scheduling client for cost optimization"""
    
    PRICING = {
        'pro': DeepSeekPricing(
            model='pro', peak_input=4.0, peak_output=12.0,
            offpeak_input=2.0, offpeak_output=6.0,
            night_input=1.6, night_output=4.8
        ),
        'flash': DeepSeekPricing(
            model='flash', peak_input=1.0, peak_output=4.0,
            offpeak_input=0.5, offpeak_output=2.0,
            night_input=0.4, night_output=1.6
        )
    }
    
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.task_queue = []
    
    def get_current_price_period(self) -> str:
        """Determine current pricing period"""
        now = datetime.datetime.now()
        hour = now.hour
        weekday = now.weekday()
        
        if weekday >= 5:  # Weekend
            return 'offpeak'
        if 23 <= hour or hour < 7:
            return 'night'
        if (12 <= hour < 14) or (18 <= hour < 23):
            return 'offpeak'
        if (9 <= hour < 12) or (14 <= hour < 18):
            return 'peak'
        return 'offpeak'
    
    def estimate_cost(self, model: str, input_tokens: int, output_tokens: int) -> dict:
        """Estimate API call cost for current period"""
        pricing = self.PRICING[model]
        period = self.get_current_price_period()
        
        input_price = getattr(pricing, f'{period}_input')
        output_price = getattr(pricing, f'{period}_output')
        
        input_cost = (input_tokens / 1_000_000) * input_price
        output_cost = (output_tokens / 1_000_000) * output_price
        
        return {
            'period': period,
            'total_cost_cny': round(input_cost + output_cost, 4),
            'input_price_per_m': input_price,
            'output_price_per_m': output_price
        }
    
    def auto_select_model(self, task_difficulty: str, input_tokens: int, 
                          output_tokens: int) -> str:
        """Auto-select model based on task difficulty and time period"""
        period = self.get_current_price_period()
        
        if task_difficulty == 'easy':
            return 'flash'
        if task_difficulty == 'hard':
            return 'pro'
        if task_difficulty == 'medium':
            if period == 'peak' and output_tokens <= 4000:
                return 'flash'
            return 'pro'
        return 'flash'
    
    def schedule_task(self, prompt: str, task_type: str = 'medium',
                      max_tokens: int = 4096,
                      deadline: Optional[datetime.datetime] = None,
                      callback: Optional[Callable] = None):
        """Schedule task with optional delay to off-peak"""
        input_tokens = len(prompt) // 2
        
        if deadline and datetime.datetime.now() + datetime.timedelta(hours=2) < deadline:
            period = self.get_current_price_period()
            if period == 'peak':
                print(f"[Scheduler] Peak hours, task deadline={deadline}")
                print(f"[Scheduler] Delaying to off-peak, saving ~50% cost")
                self.task_queue.append({
                    'prompt': prompt, 'max_tokens': max_tokens,
                    'callback': callback,
                    'scheduled_time': datetime.datetime.now() + datetime.timedelta(hours=3)
                })
                return {'status': 'queued', 'message': 'Task queued for off-peak execution'}
        
        model = self.auto_select_model(task_type, input_tokens, max_tokens)
        cost = self.estimate_cost(model, input_tokens, max_tokens)
        print(f"[Execute] Model: {model}, Period: {cost['period']}, Cost: ¥{cost['total_cost_cny']}")
        
        return {'status': 'executed', 'model': model, 'cost': cost}


# Usage example
scheduler = DeepSeekScheduler(api_key="sk-ds-v4-example")
print(f"Current Time: {datetime.datetime.now()}")
print(f"Current Period: {scheduler.get_current_price_period()}")

# Cost comparison across periods
for period in ['peak', 'offpeak', 'night']:
    cost = scheduler.estimate_cost('pro', 10000, 4000)
    print(f"[{period}] Pro: ¥{cost['output_price_per_m']}/M output")

# Queue non-urgent task during peak
far_deadline = datetime.datetime.now() + datetime.timedelta(hours=4)
result = scheduler.schedule_task(
    prompt="Analyze 1000 financial reports",
    task_type='medium', max_tokens=16384, deadline=far_deadline
)
print(f"Queue result: {result}")

// DSpark Inference Acceleration Engine
package main

import (
    "fmt"
    "sync"
    "time"
)

type DSparkConfig struct {
    EnableDynamicSparsity bool
    SpeculativeDecoding   bool
    DraftModelSize        string
    KVQuantization        string
    BatchSize             int
}

type InferenceStats struct {
    TokensPerSecond   float64
    SpecAcceptRate    float64
    EffectiveSparsity float64
}

type DSparkEngine struct {
    config DSparkConfig
    stats  InferenceStats
    mu     sync.Mutex
}

func NewDSparkEngine(config DSparkConfig) *DSparkEngine {
    return &DSparkEngine{config: config}
}

func (e *DSparkEngine) SimulateInference(promptTokens, outputTokens int) InferenceStats {
    baseTimePerToken := 15.0 * time.Millisecond
    baseTotalTime := time.Duration(float64(outputTokens) * float64(baseTimePerToken))
    speedup := 1.0
    
    if e.config.EnableDynamicSparsity {
        seqLen := promptTokens + outputTokens
        var sparsityGain float64
        if seqLen > 100000 {
            sparsityGain = 2.8
        } else if seqLen > 32000 {
            sparsityGain = 1.8
        } else {
            sparsityGain = 1.2
        }
        speedup *= sparsityGain
    }
    
    if e.config.SpeculativeDecoding {
        acceptRates := map[string]float64{"tiny": 0.75, "small": 0.85, "medium": 0.92}
        draftSpeeds := map[string]float64{"tiny": 3.2, "small": 2.5, "medium": 1.8}
        
        if rate, ok := acceptRates[e.config.DraftModelSize]; ok {
            draftSpeed := draftSpeeds[e.config.DraftModelSize]
            specSpeedup := 1.0 / (1.0/draftSpeed + (1.0-rate)/draftSpeed)
            speedup *= specSpeedup
            e.mu.Lock()
            e.stats.SpecAcceptRate = rate
            e.mu.Unlock()
        }
    }
    
    if e.config.KVQuantization != "" {
        quantGain := map[string]float64{"fp8": 1.4, "int8": 1.6}
        if gain, ok := quantGain[e.config.KVQuantization]; ok {
            speedup *= gain
        }
    }
    
    if e.config.BatchSize > 1 {
        speedup *= 1.0 + float64(e.config.BatchSize-1)*0.15
    }
    speedup *= 1.25 // Operator fusion gain
    
    acceleratedTime := time.Duration(float64(baseTotalTime) / speedup)
    tokensPerSec := float64(outputTokens) / acceleratedTime.Seconds()
    
    stats := InferenceStats{
        TokensPerSecond: tokensPerSec,
    }
    e.mu.Lock()
    e.stats = stats
    e.mu.Unlock()
    
    fmt.Printf("=== DSpark Acceleration Analysis ===\n")
    fmt.Printf("Base Speed: %.0f tokens/s\n", float64(outputTokens)/baseTotalTime.Seconds())
    fmt.Printf("Accelerated Speed: %.0f tokens/s\n", tokensPerSec)
    fmt.Printf("Total Speedup: %.1fx\n", speedup)
    fmt.Printf("Throughput Gain: %.1f%%\n", (speedup-1.0)*100)
    
    return stats
}

func main() {
    config := DSparkConfig{
        EnableDynamicSparsity: true,
        SpeculativeDecoding:   true,
        DraftModelSize:        "small",
        KVQuantization:        "fp8",
        BatchSize:             4,
    }
    engine := NewDSparkEngine(config)
    engine.SimulateInference(32000, 2048)
}

import requests

class DeepSeekV4Client:
    def __init__(self, api_key: str):
        self.api_key = api_key
        self.base_url = "https://api.deepseek.com/v4"
        self.session = requests.Session()
        self.session.headers.update({
            "Authorization": f"Bearer {api_key}",
            "Content-Type": "application/json"
        })
    
    def chat(self, messages: list, model: str = "deepseek-v4-pro",
             max_tokens: int = 4096) -> dict:
        payload = {
            "model": model,
            "messages": messages,
            "max_tokens": max_tokens,
            "temperature": 0.7
        }
        resp = self.session.post(f"{self.base_url}/chat/completions", json=payload)
        resp.raise_for_status()
        return resp.json()
    
    def agentic_coding(self, task: str) -> dict:
        system_prompt = """You are a professional AI coding assistant. For each task:
1. Analyze requirements
2. Design solution architecture
3. Write complete, runnable code
4. Add comprehensive error handling"""
        
        return self.chat([
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": f"Task: {task}"}
        ], max_tokens=16384)

client = DeepSeekV4Client(api_key="sk-ds-v4-example")
response = client.agentic_coding("Implement a distributed task scheduler in Go")
print(response)