"""
A much shorter version of train.py for benchmarking
"""
import os
import numpy as np
import time
import torch
from model import GPTConfig, GPT

device = 'cuda'
torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul
torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
torch.manual_seed(1337)

batch_size = 8
block_size = 1024

# data loading init
real_data = True
if real_data:
    dataset = 'openwebtext'
    data_dir = os.path.join('data', dataset)
    train_data = np.memmap(os.path.join(data_dir, 'train.bin'), dtype=np.uint16, mode='r')
    def get_batch(split):
        data = train_data # note ignore split in benchmarking script
        ix = torch.randint(len(data) - block_size, (batch_size,))
        x = torch.stack([torch.from_numpy((data[i:i+block_size]).astype(np.int64)) for i in ix])
        y = torch.stack([torch.from_numpy((data[i+1:i+1+block_size]).astype(np.int64)) for i in ix])
        x, y = x.to(device), y.to(device)
        return x, y
else:
    # alternatively, if fixed data is desired to not care about data loading
    x = torch.randint(50257, (batch_size, block_size), device=device)
    y = torch.randint(50257, (batch_size, block_size), device=device)
    get_batch = lambda split: (x, y)

# model init
gptconf = GPTConfig(
    block_size = block_size, # how far back does the model look? i.e. context size
    n_layer = 12, n_head = 12, n_embd = 768, # size of the model
    dropout = 0, # for determinism
)
model = GPT(gptconf)
model.to(device)

optimizer = model.configure_optimizers(weight_decay=1e-2, learning_rate=1e-4, betas=(0.9, 0.95))

profile = False # use pytorch profiler, or just simple benchmarking?
if profile:
    # useful docs on pytorch profiler:
    # - tutorial https://pytorch.org/tutorials/intermediate/tensorboard_profiler_tutorial.html
    # - api https://pytorch.org/docs/stable/profiler.html#torch.profiler.profile
    wait, warmup, active = 5, 5, 5
    num_steps = wait + warmup + active
    with torch.profiler.profile(
        activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA],
        schedule=torch.profiler.schedule(wait=wait, warmup=warmup, active=active, repeat=1),
        on_trace_ready=torch.profiler.tensorboard_trace_handler('./bench_log'),
        record_shapes=True,
        profile_memory=True,
        with_stack=True, # incurs an additional overhead, disable if not needed
        with_flops=True,
        with_modules=False, # only for torchscript models atm
    ) as prof:

        for k in range(num_steps):
            X, Y = get_batch('train')
            with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
                logits, loss = model(X, Y)
            optimizer.zero_grad(set_to_none=True)
            loss.backward()
            optimizer.step()
            lossf = loss.item()
            print(f"{k}/{num_steps} loss: {lossf:.4f}")

            prof.step() # notify the profiler at end of each step

else:

    # simple benchmarking
    torch.cuda.synchronize()
    for stage, num_steps in enumerate([10, 20]): # burnin, then benchmark
        t0 = time.time()
        for k in range(num_steps):
            X, Y = get_batch('train')
            with torch.autocast(device_type='cuda', dtype=torch.bfloat16):
                logits, loss = model(X, Y)
            optimizer.zero_grad(set_to_none=True)
            loss.backward()
            optimizer.step()
            lossf = loss.item()
            print(f"{k}/{num_steps} loss: {lossf:.4f}")
        torch.cuda.synchronize()
        t1 = time.time()
        if stage == 1:
            print(f"time per iteration: {(t1-t0)/num_steps*1000:.4f}ms")