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https://github.com/deepseek-ai/FlashMLA
synced 2025-06-26 18:15:54 +00:00
add fp8 ut
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@@ -37,7 +37,7 @@ def cal_diff(x: torch.Tensor, y: torch.Tensor, name: str) -> None:
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@torch.inference_mode()
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def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen):
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def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen, use_fp8 = False):
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print(f"{b=}, {s_q=}, {mean_sk=}, {h_q=}, {h_kv=}, {d=}, {dv=}, {causal=}, {varlen=}")
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cache_seqlens = torch.full((b,), mean_sk, dtype=torch.int32)
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@@ -59,11 +59,28 @@ def test_flash_mla(b, s_q, mean_sk, h_q, h_kv, d, dv, causal, varlen):
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blocked_v = blocked_k[..., :dv]
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tile_scheduler_metadata, num_splits = get_mla_metadata(cache_seqlens, s_q * h_q // h_kv, h_kv)
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descale_q, descale_k = None, None
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if use_fp8:
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fp8_dtype = torch.float8_e4m3fn
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descale_q = torch.ones((b), dtype=torch.float32)
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descale_k = torch.ones((b), dtype=torch.float32)
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q_fp8 = q.to(fp8_dtype)
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blocked_k_fp8 = blocked_k.to(fp8_dtype)
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blocked_v_fp8 = blocked_v.to(fp8_dtype)
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q = q_fp8.to(q.dtype)
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blocked_k = blocked_k_fp8.to(blocked_k.dtype)
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blocked_v = blocked_v_fp8.to(blocked_v.dtype)
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def flash_mla():
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q_ = q_fp8 if use_fp8 else q
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blocked_k_ = blocked_k_fp8 if use_fp8 else blocked_k
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return flash_mla_with_kvcache(
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q, blocked_k, block_table, cache_seqlens, dv,
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q_, blocked_k_, block_table, cache_seqlens, dv,
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tile_scheduler_metadata, num_splits, causal=causal,
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descale_q=descale_q, descale_k=descale_k,
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)
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def ref_mla():
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@@ -107,10 +124,11 @@ if __name__ == "__main__":
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h_kv = 1
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d, dv = 576, 512
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causal = True
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use_fp8 = False
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for b in [128]:
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for s in [4096, 8192]:
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for h_q in [16, 32, 64, 128]: # TP = 8, 4, 2, 1
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for s_q in [1, 2]: # MTP = 1, 2
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for varlen in [False, True]:
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test_flash_mla(b, s_q, s, h_q, h_kv, d, dv, causal, varlen)
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test_flash_mla(b, s_q, s, h_q, h_kv, d, dv, causal, varlen, use_fp8)
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