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https://github.com/ml-explore/mlx.git
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a3c287354f
* Working hadamard for powers of 2 * working for m*2^k * add scale and check contiguity * add size check * clean up * fix test * add grads + vmap * gpu only * skip on linux * test typo * add cpu impl * remove gpu only tests * fix linux build + add is_equivalent
71 lines
1.9 KiB
Python
71 lines
1.9 KiB
Python
import argparse
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import matplotlib
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import mlx.core as mx
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import numpy as np
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from time_utils import measure_runtime
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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def had(x):
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y = mx.hadamard_transform(x)
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mx.eval(y)
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def copy(x):
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y = x + 1.0
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mx.eval(y)
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def run(dtype):
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system_size = 2**26
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outputs = {}
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for test_fn in (had, copy):
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for m in [1, 12, 20, 28]:
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if test_fn == copy:
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key = "copy"
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elif m == 1:
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key = "had_2^k"
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else:
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key = "had_m*2^k"
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outputs.setdefault(key, {})
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for k in range(7, 14):
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n = m * 2**k
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if n > 2**15:
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continue
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x_np = np.random.normal(size=(system_size // n, n)).astype(dtype)
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x = mx.array(x_np)
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runtime_ms = measure_runtime(test_fn, x=x)
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bytes_per_gb = 1e9
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ms_per_s = 1e3
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bytes_per_had = np.dtype(x_np.dtype).itemsize * 2
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bandwidth_gb = (
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system_size * bytes_per_had / runtime_ms * ms_per_s / bytes_per_gb
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)
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print(n, bandwidth_gb)
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outputs[key][n] = bandwidth_gb
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colors = {
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"copy": "black",
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"had_2^k": "steelblue",
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"had_m*2^k": "skyblue",
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}
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for key, output in outputs.items():
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plt.scatter(output.keys(), output.values(), color=colors[key], label=key)
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plt.title(f"MLX Hadamard Benchmark -- {dtype.__name__}")
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plt.xlabel("N")
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plt.ylabel("Bandwidth (GB/s)")
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plt.legend()
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plt.savefig(f"bench_{dtype.__name__}.png")
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plt.clf()
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument("--fp16", action="store_true")
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args = parser.parse_args()
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dtype = np.float16 if args.fp16 else np.float32
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run(dtype)
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