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import matplotlib.pyplot as plt
import time
import torch
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import numpy as np
import tracemalloc
# from Andromeda.model import Andromeda
from Andromeda.model import Andromeda
from Andromeda.utils.stable_adamw import StableAdamWUnfused
torch.manual_seed(0)
if torch.cuda.is_available():
torch.cuda.manual_seed(0)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class AndromedaModelTest:
def __init__(self):
self.model = Andromeda
self.optimizer = StableAdamWUnfused()
self.loss_function = torch.nn.CrossEntropyLoss()
self.test_input = torch.randint(0, 256, (1, 1024)).cuda()
def test_forward_pass(self):
output = self.model(self.test_input)
assert output.shape == (1, 1024, 64007), "Forward pass output shape mismatch"
def test_backward_pass(self):
self.optimizer.zero_grad()
output = self.model(self.test_input)
loss = self.loss_function(output, self.test_input)
loss.backward()
for name, parameter in self.model.named_parameters():
assert not torch.isnan(parameter.grad().any()), f"Gradient for {name} contains NaNs"
assert not torch.isinf(parameter.grad().any()), f"Gradient for {name} contains Infs"
def test_optimizer_step(self):
initial_params = [param.clone() for param in self.model_parameters()]
output = self.model(self.test_input)
loss = self.loss_function(output, self.test_input)
self.optimizer.zero_grad()
loss.backward()
self.optimizer.step()
for initial_param, param in zip(initial_params, self.model.parameters()):
assert not torch.equal(initial_param, param), "Model Parameters did not change after an optimizer step"
class SpeedMetrics:
def __init__(self, model):
self.model = model.to(device)
def forward_pass_time(self):
start_time = time.time()
self.model.decoder.forward(torch.randint(0, 50304, (1, 8192), device=device, dtype=torch.long))[0]
end_time = time.time()
return end_time - start_time
def backward_pass_time(self):
model_input = self.model.decoder.forward(torch.randint(0, 50304, (1, 8192), device=device, dtype=torch.long))[0]
start_time = time.time()
loss = torch.nn.CrossEntropyLoss()(model_input, torch.randint(0, 50304, (1, 8192), device=device, dtype=torch.long))
loss.backward()
end_time = time.time()
return end_time - start_time
def end_to_end_latency(self):
start_time = time.time()
self.model.forward(torch.randint(0, 50304, (1, 8192), device=device, dtype=torch.long))
end_time = time.time()
return end_time - start_time
class ScalabilityMetrics:
def __init__(self, model, dataset):
self.model = model
self.dataset = dataset
self.dataloader = DataLoader(dataset, batch_size=32)
def throughput(self):
start_time = time.time()
for i, data in enumerate(self.dataloader, 0):
self.model.forward(data)
end_time = time.time()
return len(self.dataset) / (end_time - start_time)
class ConsistencyMetrics:
def __init__(self, model):
self.model = model
def consistency_over_time(self):
consistency_times = []
outputs_list = []
for _ in range(10):
start_time = time.time()
outputs = self.model.forward(torch.randint(0, 50304, (1, 8192)))
end_time = time.time()
consistency_times.append(end_time - start_time)
outputs_list.append(outputs.detach().numpy())
initial_output = outputs_list[0]
consistency_score = 0
for output in outputs_list[1:]:
if np.array_equal(initial_output, output):
consistency_score += 1
consistency_score = consistency_score / len(outputs_list) * 100
return consistency_times, consistency_score
class MemoryMetrics:
def __init__(self, model):
self.model = model
def memory_footprint(self):
tracemalloc.start()
self.model.forward(torch.randint(0, 50304, (1, 8192)))
current, peak = tracemalloc.get_traced_memory()
tracemalloc.stop()
return current, peak
class SequenceMetrics:
def __init__(self, model):
self.model = model
def sequence_length_impact(self):
seq_lengths = [1024, 2048, 4096, 8192]
seq_impact_times = []
for length in seq_lengths:
start_time = time.time()
self.model.forward(torch.randint(0, 50304, (1, length)))
end_time = time.time()
seq_impact_times.append(end_time - start_time)
return seq_lengths, seq_impact_times
class FlopsBenchmark:
def __init__(self, model, bsz=32, d_model=1024, num_heads=8, sequence_lengths=list(range(500, 32001, 500))):
self.bsz = bsz
self.d_model = d_model
self.num_heads = num_heads
self.sequence_lengths = sequence_lengths
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.dtype=torch.float32
self.model = model.to(self.device)
def benchmark(self):
time_taken = []
tflops_per_s = []
for seq_len in self.sequence_lengths:
x = torch.randn(self.bsz, seq_len, self.d_model).to(self.device).type(self.dtype)
torch.cuda.synchronize()
start = time.time()
self.model(x)
torch.cuda.synchronize()
elapsed = time.time() - start
time_taken.append(elapsed)
total_flops = 4 * seq_len **2 * (self.d_model // self.num_heads) * self.num_heads
tflops_per_s.append(total_flops / elapsed / 1e12) # Convert to TFLOPs
for seq_len, elapsed, tflops in zip(self.sequence_lengths, time_taken, tflops_per_s):
print(f"Sequence length: {seq_len}, Time elapsed: {elapsed} s, TFLOPs/s: {tflops}")
#mock test dataset
test_dataset = datasets.FakeData(size=1000, transform=transforms.ToTensor())
#model
model = Andromeda(
num_tokens=50304,
dim=1024,
depth=24,
dim_head=128,
heads=8,
alibi_num_heads=4
)
#speed test metrics test
# speed test metrics test
speed_metrics = SpeedMetrics(model)
forward_pass_time = speed_metrics.forward_pass_time()
backward_pass_time = speed_metrics.backward_pass_time()
end_to_end_latency = speed_metrics.end_to_end_latency()
#scalability metrics test
scalability_metrics = ScalabilityMetrics(model, test_dataset)
throughput = scalability_metrics.throughput()
#consistency metrucs test
consistency_metrics = ConsistencyMetrics(model)
consistency_times, consistency_score = consistency_metrics.consistency_over_time()
#memory metrics test
memory_metrics = MemoryMetrics(model)
current, peak = memory_metrics.memory_footprint()
#sequence metrics test
sequence_metrics = SequenceMetrics(model)
seq_lengths, seq_impact_times = sequence_metrics.sequence_length_impact()
#flops
flops_benchmark = FlopsBenchmark(model)
flops_benchmark.benchmark()
# Graphical Interface
fig, axs = plt.subplots(3)
axs[0].bar(["Forward Pass Time", "Backward Pass Time", "End-to-End Latency"], [forward_pass_time, backward_pass_time, end_to_end_latency])
axs[0].set_title('Speed Metrics')
axs[0].set_xlabel('Metrics')
axs[0].set_ylabel('Time (seconds)')
axs[1].bar(seq_lengths, seq_impact_times)
axs[1].set_title('Sequence Length Impact')
axs[1].set_xlabel('Sequence Length')
axs[1].set_ylabel('Time (seconds)')
axs[2].plot(list(range(1, 11)), consistency_times)
axs[2].set_title('Consistency Over Time')
axs[2].set_xlabel('Run Number')
axs[2].set_ylabel('Time (seconds)')
plt.tight_layout()
plt.show()
print(f"Throughput: {throughput} instances/second")
print(f"Memory used: {current / 10**6}MB; Peak: {peak / 10**6}MB")
# Add at the bottom of your file
if __name__ == "__main__":
model_test = AndromedaModelTest()
model_test.test_forward_pass()
model_test.test_backward_pass()
model_test.test_optimizer_step() |