biondizzle
f52eedbdce
Previous unit tests used toy values (HD=64-256, T=16, small N). These tests validate the actual production configuration: - FMHA: HD=512, 128 Q heads, N=128/2048/8192 - Compression: CSA T=4096, HCA T=16384, full 1M context - NVFP4: production weight shapes (q_a, kv, wo_a, gate) - MoE: 384 experts, top-6, 3072 intermediate - mHC: 4 streams, 61 layers, residual bounded, doubly-stochastic - Router: 384 experts hash + noaux-TC - Memory budget: 1M context KV pool, 8-GPU weight distribution
476 lines
21 KiB
Python
476 lines
21 KiB
Python
#!/usr/bin/env python3
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"""Production-value tests for DSV4 Pro kernel stack.
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ALL tests use Pro config values:
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- 61 layers, 7168 hidden, 128 query heads, HD=512
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- 384 routed experts, top-6, 3072 intermediate
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- HCA ratio=128, CSA ratio=4, CSA top-k=1024
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- 4-way mHC, 20 Sinkhorn iters
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- SWA window=128
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This file is the ONLY acceptable place for non-production test values.
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If a test needs a smaller value for memory/time, it must be marked
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with a comment explaining why and what the production value should be.
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"""
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import math
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import torch
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import pytest
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# ─── Production Pro config ───────────────────────────────────────────
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PRO = dict(
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num_layers=61,
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hidden_size=7168,
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num_query_heads=128,
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head_dim=512,
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rope_dim=64,
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query_compression_dim=1536,
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csa_compression_ratio=4,
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csa_top_k=1024,
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indexer_num_heads=64,
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indexer_head_dim=128,
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hca_compression_ratio=128,
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sliding_window=128,
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num_output_groups=16,
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output_group_dim=1024,
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num_routed_experts=384,
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num_shared_experts=1,
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num_experts_per_tok=6,
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moe_intermediate_size=3072,
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num_hash_routing_layers=3,
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routed_scaling_factor=2.5,
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n_hc=4,
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sinkhorn_iters=20,
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rms_norm_eps=1e-6,
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)
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DEVICE = "cuda:0" if torch.cuda.is_available() else "cpu"
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# ─── 1. FMHA at HD=512, production head counts ──────────────────────
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class TestFMHAProduction:
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"""FMHA tests at Pro config: HD=512, 128 query heads, various KV lengths."""
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_fmha_hd512_decode_short(self):
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"""Decode (T=1) with 128 Q heads, HD=512, N=128 (1 SWA window)."""
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n_q = PRO["num_query_heads"]
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hd = PRO["head_dim"]
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N = PRO["sliding_window"]
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T = 1
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(T, n_q, hd, dtype=torch.bfloat16, device=DEVICE)
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k = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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v = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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# Reference: PyTorch SDPA
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q_4d = q.reshape(1, n_q, T, hd)
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k_4d = k.reshape(1, 1, N, hd).expand(1, n_q, N, hd)
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v_4d = v.reshape(1, 1, hd, N).expand(1, n_q, hd, N)
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ref = torch.nn.functional.scaled_dot_product_attention(
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q_4d.float(), k_4d.float(), v_4d.float().transpose(-2, -1), scale=scale
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).bfloat16() # (1, n_q, T, hd)
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from dsv4.layers.attention import _run_production_fmha
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prod = _run_production_fmha(q, k.unsqueeze(0), v.unsqueeze(0), n_q, hd, T, N, scale, DEVICE, 0, "swa", "swa")
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cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
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assert cos > 0.999, f"FMHA HD=512 decode short: cos={cos:.6f}"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_fmha_hd512_decode_medium(self):
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"""Decode (T=1) with HD=512, N=2048 (compressed tokens after HCA)."""
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n_q = PRO["num_query_heads"]
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hd = PRO["head_dim"]
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N = 2048 # typical compressed KV length after HCA at moderate context
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T = 1
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(T, n_q, hd, dtype=torch.bfloat16, device=DEVICE)
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k = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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v = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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q_4d = q.reshape(1, n_q, T, hd)
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k_4d = k.reshape(1, 1, N, hd).expand(1, n_q, N, hd)
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v_4d = v.reshape(1, 1, hd, N).expand(1, n_q, hd, N)
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ref = torch.nn.functional.scaled_dot_product_attention(
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q_4d.float(), k_4d.float(), v_4d.float().transpose(-2, -1), scale=scale
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).bfloat16()
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from dsv4.layers.attention import _run_production_fmha
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prod = _run_production_fmha(q, k.unsqueeze(0), v.unsqueeze(0), n_q, hd, T, N, scale, DEVICE, 0, "hca", "hca")
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cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
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assert cos > 0.999, f"FMHA HD=512 decode medium: cos={cos:.6f}"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_fmha_hd512_decode_long(self):
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"""Decode (T=1) with HD=512, N=8192 (compressed tokens at long context)."""
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n_q = PRO["num_query_heads"]
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hd = PRO["head_dim"]
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N = 8192 # compressed KV after HCA at ~1M context (1M/128=7812)
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T = 1
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(T, n_q, hd, dtype=torch.bfloat16, device=DEVICE)
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k = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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v = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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q_4d = q.reshape(1, n_q, T, hd)
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k_4d = k.reshape(1, 1, N, hd).expand(1, n_q, N, hd)
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v_4d = v.reshape(1, 1, hd, N).expand(1, n_q, hd, N)
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ref = torch.nn.functional.scaled_dot_product_attention(
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q_4d.float(), k_4d.float(), v_4d.float().transpose(-2, -1), scale=scale
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).bfloat16()
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from dsv4.layers.attention import _run_production_fmha
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prod = _run_production_fmha(q, k.unsqueeze(0), v.unsqueeze(0), n_q, hd, T, N, scale, DEVICE, 0, "hca", "hca")
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cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
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assert cos > 0.999, f"FMHA HD=512 decode long: cos={cos:.6f}"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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@pytest.mark.parametrize("N", [512, 1024, 4096])
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def test_fmha_hd512_csa_topk(self, N):
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"""Decode with CSA top-k=1024 selected tokens, HD=512."""
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n_q = PRO["num_query_heads"]
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hd = PRO["head_dim"]
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T = 1
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scale = 1.0 / math.sqrt(hd)
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q = torch.randn(T, n_q, hd, dtype=torch.bfloat16, device=DEVICE)
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k = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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v = torch.randn(N, hd, dtype=torch.bfloat16, device=DEVICE)
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q_4d = q.reshape(1, n_q, T, hd)
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k_4d = k.reshape(1, 1, N, hd).expand(1, n_q, N, hd)
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v_4d = v.reshape(1, 1, hd, N).expand(1, n_q, hd, N)
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ref = torch.nn.functional.scaled_dot_product_attention(
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q_4d.float(), k_4d.float(), v_4d.float().transpose(-2, -1), scale=scale
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).bfloat16()
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from dsv4.layers.attention import _run_production_fmha
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prod = _run_production_fmha(q, k.unsqueeze(0), v.unsqueeze(0), n_q, hd, T, N, scale, DEVICE, 0, "csa", "csa")
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cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
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assert cos > 0.999, f"FMHA HD=512 CSA N={N}: cos={cos:.6f}"
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# ─── 2. Compression at production scale ─────────────────────────────
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class TestCompressionProduction:
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"""CSA and HCA compression at production token counts and ratios."""
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_csa_compress_production_scale(self):
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"""CSA: ratio=4, T=4096 tokens → 1024 compressed, HD=512."""
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hd = PRO["head_dim"]
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m = PRO["csa_compression_ratio"] # 4
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T = PRO["csa_top_k"] * m # 4096
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n_blocks = T // m
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kv = torch.randn(T, 2 * hd, dtype=torch.float32, device=DEVICE) * 3.0
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gate = torch.randn(T, 2 * hd, dtype=torch.float32, device=DEVICE)
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# Reference: block-wise softmax + weighted sum
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Ca = kv[:, :hd].reshape(n_blocks, m, hd)
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Cb = kv[:, hd:].reshape(n_blocks, m, hd)
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Ga = gate[:, :hd].reshape(n_blocks, m, hd)
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Gb = gate[:, hd:].reshape(n_blocks, m, hd)
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ref_a = torch.zeros(n_blocks, hd, device=DEVICE)
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ref_b = torch.zeros(n_blocks, hd, device=DEVICE)
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for b in range(n_blocks):
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sa = torch.softmax(Ga[b], dim=0)
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sb = torch.softmax(Gb[b], dim=0)
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ref_a[b] = (sa * Ca[b]).sum(0)
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ref_b[b] = (sb * Cb[b]).sum(0)
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ref = torch.cat([ref_a, ref_b], dim=-1)
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from dsv4.kernels.compressor.production_compress import csa_compress_production
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prod = csa_compress_production(kv.bfloat16(), gate.bfloat16(), None, None, m=m)
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cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
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assert cos > 0.999, f"CSA compress production scale: cos={cos:.6f}"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_hca_compress_production_scale(self):
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"""HCA: ratio=128, T=16384 tokens → 128 compressed, HD=512.
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This is the 1M context enabler: 1M tokens / 128 = 7812 compressed tokens.
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We test a single HCA block here.
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"""
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hd = PRO["head_dim"]
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m = PRO["hca_compression_ratio"] # 128
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T = m * 128 # 16384 tokens → 128 compressed
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n_blocks = T // m
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kv = torch.randn(T, hd, dtype=torch.float32, device=DEVICE) * 3.0
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gate = torch.randn(T, hd, dtype=torch.float32, device=DEVICE)
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ref = []
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for b in range(n_blocks):
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block_kv = kv[b*m:(b+1)*m]
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block_gate = gate[b*m:(b+1)*m]
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probs = torch.softmax(block_gate, dim=0)
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ref.append((probs * block_kv).sum(0))
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ref = torch.stack(ref)
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from dsv4.kernels.compressor.production_compress import hca_compress_production
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prod = hca_compress_production(kv.bfloat16(), gate.bfloat16(), None, None, m=m)
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cos = torch.nn.functional.cosine_similarity(ref.flatten().float(), prod.flatten().float(), dim=0).item()
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assert cos > 0.999, f"HCA compress production scale: cos={cos:.6f}"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_hca_compress_1m_context(self):
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"""HCA at full 1M context scale: 1M tokens, ratio=128 → 7812 compressed.
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This tests that the kernel handles the full production token count
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without OOM or numerical issues.
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"""
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hd = PRO["head_dim"]
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m = PRO["hca_compression_ratio"] # 128
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T = 1_000_000 # 1M context
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n_blocks = T // m # 7812
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# Use smaller data to avoid OOM on test — but validate at correct n_blocks
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# The kernel processes blocks independently, so correctness at n_blocks=7812
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# with random data proves the indexing is correct
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kv = torch.randn(T, hd, dtype=torch.bfloat16, device=DEVICE) * 3.0
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gate = torch.randn(T, hd, dtype=torch.bfloat16, device=DEVICE)
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from dsv4.kernels.compressor.production_compress import hca_compress_production
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prod = hca_compress_production(kv, gate, None, None, m=m)
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assert prod.shape[0] == n_blocks, f"Expected {n_blocks} compressed, got {prod.shape[0]}"
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assert prod.shape[1] == hd, f"Expected hd={hd}, got {prod.shape[1]}"
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assert torch.isfinite(prod).all(), "HCA compress 1M: NaN/Inf in output"
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# ─── 3. NVFP4 GEMM at production weight shapes ─────────────────────
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class TestNVFP4GEMMProduction:
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"""Test NVFP4 linear layers at Pro model weight shapes."""
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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@pytest.mark.parametrize("name,in_dim,out_dim", [
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("q_a_proj", 7168, 1536), # hidden → query compression
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("kv_proj", 7168, 2*512), # hidden → KV (1 KV head for GQA)
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("wo_a_proj", 16*1024, 7168), # output groups → hidden
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("gate_proj", 7168, 3072*384), # MoE gate: hidden → 384 experts (for dense router)
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])
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def test_nvfp4_linear_production_shapes(self, name, in_dim, out_dim):
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"""Test Nvfp4Linear at actual Pro model weight dimensions."""
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from dsv4.layers.linear import Nvfp4Linear
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# kv_proj in GQA has fewer heads — the actual out_dim varies per layer
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# but the kernel must handle all shapes
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lin = Nvfp4Linear(in_dim, out_dim, max_num_tokens=8192, device=DEVICE)
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x = torch.randn(1, in_dim, dtype=torch.bfloat16, device=DEVICE) * 2.0
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out = lin(x)
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assert out.shape == (1, out_dim), f"Expected (1, {out_dim}), got {out.shape}"
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assert torch.isfinite(out).all(), f"NaN/Inf in {name} output"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_nvfp4_moe_384_experts(self):
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"""Test Nvfp4MoE with 384 routed experts, top-6, 3072 intermediate."""
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from dsv4.layers.ffn import Nvfp4MoE
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H = PRO["hidden_size"]
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E = PRO["num_routed_experts"]
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K = PRO["num_experts_per_tok"]
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I = PRO["moe_intermediate_size"]
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moe = Nvfp4MoE(num_experts=E, hidden_size=H, intermediate_size=I, top_k=K, device=DEVICE)
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x = torch.randn(1, H, dtype=torch.bfloat16, device=DEVICE) * 2.0
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topk_ids = torch.randint(0, E, (1, K), device=DEVICE, dtype=torch.int32)
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topk_weights = torch.softmax(torch.randn(1, K, device=DEVICE), dim=-1)
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out = moe.run(x, topk_ids, topk_weights)
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assert out.shape == (1, H), f"Expected (1, {H}), got {out.shape}"
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assert torch.isfinite(out).all(), "NaN/Inf in MoE output"
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# ─── 4. mHC at production depth ─────────────────────────────────────
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class TestMHCProduction:
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"""Test multi-head hyper-connection with 4 streams, 61 layers, Sinkhorn."""
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_mhc_61_layers_residual_bounded(self):
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"""Run mHC through 61 layers and verify residual stays bounded.
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Production mHC should keep |X| bounded. If it grows unbounded,
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the Sinkhorn normalization is wrong.
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"""
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from dsv4.layers.mhc import mHCLayer
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H = PRO["hidden_size"]
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n_hc = PRO["n_hc"]
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n_layers = PRO["num_layers"]
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eps = PRO["rms_norm_eps"]
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# Simulate 61 layers of mHC with random weights
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x = torch.randn(n_hc, H, dtype=torch.bfloat16, device=DEVICE) * 0.5
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residual_norms = [x.abs().max().item()]
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for li in range(n_layers):
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layer = mHCLayer(H, n_hc, device=DEVICE)
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# Fake sub-layer output
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sub_out = torch.randn(H, dtype=torch.bfloat16, device=DEVICE) * 0.5
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x = layer(sub_out, x)
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max_val = x.abs().max().item()
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residual_norms.append(max_val)
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# mHC with proper Sinkhorn should keep residuals bounded
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# Allow generous bound (1000) but flag if growing monotonically
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final_norm = residual_norms[-1]
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max_norm = max(residual_norms)
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print(f"Residual norms: L0={residual_norms[0]:.1f} ... L61={final_norm:.1f} max={max_norm:.1f}")
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# The residual should NOT grow by >100x from input
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growth = max_norm / (residual_norms[0] + 1e-6)
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assert growth < 100, f"mHC residual grew {growth:.1f}x over 61 layers — Sinkhorn broken?"
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_mhc_sinkhorn_doubly_stochastic(self):
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"""Verify Sinkhorn produces doubly-stochastic matrices at production scale."""
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n_hc = PRO["n_hc"]
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iters = PRO["sinkhorn_iters"]
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B = 16 # Production batch dimension
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comb = torch.randn(B, n_hc, n_hc, dtype=torch.bfloat16, device=DEVICE) * 2.0
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# Sinkhorn: softmax → alternate row/col norm
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P = torch.softmax(comb.float(), dim=-1) + 1e-6
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for _ in range(iters):
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P = P / P.sum(dim=-1, keepdim=True) # row norm
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P = P / P.sum(dim=-2, keepdim=True) # col norm
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row_sums = P.sum(dim=-1)
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col_sums = P.sum(dim=-2)
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assert torch.allclose(row_sums, torch.ones_like(row_sums), atol=1e-2), \
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f"Row sums not ~1.0: {row_sums.mean().item():.4f}"
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assert torch.allclose(col_sums, torch.ones_like(col_sums), atol=1e-2), \
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f"Col sums not ~1.0: {col_sums.mean().item():.4f}"
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# ─── 5. Router at production scale ──────────────────────────────────
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class TestRouterProduction:
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"""Test router with 384 experts, hash routing for L0-2, noaux_tc for L3+."""
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@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
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def test_hash_router_384_experts(self):
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"""Hash routing (layers 0-2) with 384 experts, top-6."""
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from dsv4.layers.router import HashRouter
|
||
|
||
E = PRO["num_routed_experts"]
|
||
K = PRO["num_experts_per_tok"]
|
||
H = PRO["hidden_size"]
|
||
|
||
router = HashRouter(num_experts=E, top_k=K, hidden_size=H, device=DEVICE)
|
||
token_ids = torch.tensor([1, 50, 100, 500, 9999, 50000], dtype=torch.int32, device=DEVICE)
|
||
x = torch.randn(len(token_ids), H, dtype=torch.bfloat16, device=DEVICE) * 2.0
|
||
|
||
topk_ids, topk_weights = router(x, token_ids)
|
||
assert topk_ids.shape == (len(token_ids), K)
|
||
assert (topk_ids >= 0).all() and (topk_ids < E).all(), \
|
||
f"Expert IDs out of range: min={topk_ids.min()}, max={topk_ids.max()}"
|
||
|
||
@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
|
||
def test_noaux_tc_router_384_experts(self):
|
||
"""Noaux-TC routing (layers 3+) with 384 experts, top-6."""
|
||
from dsv4.layers.router import Router
|
||
|
||
E = PRO["num_routed_experts"]
|
||
K = PRO["num_experts_per_tok"]
|
||
H = PRO["hidden_size"]
|
||
|
||
router = Router(hidden_size=H, num_experts=E, top_k=K, device=DEVICE, is_hash=False)
|
||
x = torch.randn(1, H, dtype=torch.bfloat16, device=DEVICE) * 2.0
|
||
|
||
topk_ids, topk_weights = router.run(x)
|
||
assert topk_ids.shape == (1, K)
|
||
assert (topk_ids >= 0).all() and (topk_ids < E).all(), \
|
||
f"Expert IDs out of range: min={topk_ids.min()}, max={topk_ids.max()}"
|
||
|
||
|
||
# ─── 6. Memory budget at production scale ───────────────────────────
|
||
|
||
class TestMemoryBudget:
|
||
"""Verify memory usage stays within bounds for 1M context."""
|
||
|
||
@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
|
||
def test_kv_pool_memory_1m_context(self):
|
||
"""Calculate and validate KV pool memory at 1M context.
|
||
|
||
At 1M tokens with HCA ratio=128:
|
||
- HCA compressed: 1M / 128 = 7812 tokens × HD=512 × 2 (K+V) × 2 bytes
|
||
- SWA window: 128 tokens × HD=512 × 2 × 2 bytes
|
||
- CSA top-k: 1024 tokens × HD=512 × 2 × 2 bytes
|
||
|
||
Total per layer per batch ≈ (7812 + 128 + 1024) × 512 × 2 × 2 ≈ 18.4 MB
|
||
× 61 layers = 1.1 GB per batch — feasible on B200 192GB
|
||
"""
|
||
hca_compressed = 1_000_000 // PRO["hca_compression_ratio"] # 7812
|
||
swa_tokens = PRO["sliding_window"] # 128
|
||
csa_tokens = PRO["csa_top_k"] # 1024
|
||
hd = PRO["head_dim"]
|
||
bytes_per_val = 2 # BF16
|
||
|
||
total_tokens = hca_compressed + swa_tokens + csa_tokens
|
||
bytes_per_layer = total_tokens * hd * 2 * bytes_per_val # K+V
|
||
total_bytes = bytes_per_layer * PRO["num_layers"]
|
||
total_gb = total_bytes / 1e9
|
||
|
||
# Without compression: 1M × 512 × 2 × 2 × 61 = 125 GB — IMPOSSIBLE
|
||
uncompressed_gb = (1_000_000 * hd * 2 * bytes_per_val * PRO["num_layers"]) / 1e9
|
||
|
||
print(f"Compressed KV pool: {total_gb:.2f} GB")
|
||
print(f"Uncompressed KV pool: {uncompressed_gb:.2f} GB")
|
||
print(f"Compression saves: {uncompressed_gb - total_gb:.2f} GB ({(1 - total_gb/uncompressed_gb)*100:.1f}%)")
|
||
|
||
# Verify compression achieves the claimed ratio
|
||
assert total_gb < 5.0, f"Compressed KV too large: {total_gb:.2f} GB — compression broken?"
|
||
assert total_gb < uncompressed_gb * 0.02, "Compression ratio worse than expected"
|
||
|
||
@pytest.mark.skipif(not torch.cuda.is_available(), reason="no GPU")
|
||
def test_weight_memory_8gpu(self):
|
||
"""Validate weight distribution across 8 GPUs at Pro scale.
|
||
|
||
Pro model weight memory (NVFP4):
|
||
- 61 layers × (attention + MoE + shared expert + mHC + norms)
|
||
- NVFP4: 2 bits per param → ~0.25 bytes per param
|
||
- Total params: ~1.8T → ~450 GB in NVFP4
|
||
- Across 8 GPUs: ~56 GB per GPU — fits in B200 192GB HBM
|
||
"""
|
||
# Rough estimate: Pro has ~1.8T params (384 experts × 7168 × 3072 × 2 × 61 layers)
|
||
expert_params = PRO["num_routed_experts"] * PRO["hidden_size"] * PRO["moe_intermediate_size"] * 2 # gate+up
|
||
expert_params += PRO["num_routed_experts"] * PRO["moe_intermediate_size"] * PRO["hidden_size"] # down
|
||
shared_params = PRO["hidden_size"] * PRO["moe_intermediate_size"] * 3 # gate+up+down
|
||
attn_params = PRO["hidden_size"] * (PRO["query_compression_dim"] + 2 * PRO["head_dim"] + PRO["num_output_groups"] * PRO["output_group_dim"])
|
||
mhc_params = PRO["n_hc"] * PRO["n_hc"] * 3 + PRO["n_hc"] * 2 # comb + pre + post
|
||
|
||
total_params = (expert_params + shared_params + attn_params + mhc_params) * PRO["num_layers"]
|
||
total_params += PRO["hidden_size"] * PRO["vocab_size"] # embedding + lm_head
|
||
|
||
nvfp4_bytes = total_params / 4 # 2 bits per param
|
||
per_gpu_bytes = nvfp4_bytes / 8
|
||
per_gpu_gb = per_gpu_bytes / 1e9
|
||
|
||
print(f"Total params: {total_params/1e12:.2f}T")
|
||
print(f"NVFP4 weight memory: {nvfp4_bytes/1e9:.2f} GB total, {per_gpu_gb:.2f} GB per GPU")
|
||
|
||
assert per_gpu_gb < 100, f"Per-GPU weight memory too large: {per_gpu_gb:.2f} GB"
|
||
|
||
|
||
if __name__ == "__main__":
|
||
pytest.main([__file__, "-v", "--tb=short"])
|