[metrics] Fix prefill FLOPs estimate to count prefix and per-request causal pairs (#34316)
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@@ -459,6 +459,14 @@ class SchedulerMetricsReporter:
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num_attn_heads * head_dim * act_bytes * num_layers
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)
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@staticmethod
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def _prefill_attention_pairs(batch) -> float:
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"""Causal query-key pairs: each chunk against its cached prefix, plus
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the causal pairs within the chunk itself."""
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prefix_pairs = sum(c * p for c, p in zip(batch.extend_lens, batch.prefix_lens))
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within_chunk_pairs = sum(c * (c + 1) / 2.0 for c in batch.extend_lens)
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return float(prefix_pairs + within_chunk_pairs)
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def _estimate_prefill_perf(self, batch) -> Tuple[float, float, float]:
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if batch is None or batch.extend_lens is None:
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return 0.0, 0.0, 0.0
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@@ -466,17 +474,20 @@ class SchedulerMetricsReporter:
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if tokens == 0:
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return 0.0, 0.0, 0.0
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# Causal prefill token-context product.
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context_product = tokens * (tokens + 1) / 2.0
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context_product = self._prefill_attention_pairs(batch)
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flops = (
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tokens * self._linear_flops_per_token
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+ self._attn_dot_flops_coeff * context_product
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)
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# The chunk's queries share one pass over the cached prefix, so charge the
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# prefix KV once per chunk -- not once per query-key pair.
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prefix_kv_tokens = float(sum(batch.prefix_lens))
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read_bytes = (
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tokens * self._weight_read_bytes_per_token
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+ tokens * self._qkv_act_bytes_per_token
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+ tokens * self._prefill_attn_act_read_per_token
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+ prefix_kv_tokens * self._kv_cache_bytes_per_token
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)
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write_bytes = (
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tokens * self._kv_cache_bytes_per_token
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@@ -512,8 +523,8 @@ class SchedulerMetricsReporter:
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def _prefill_sol_suffix(self, batch, elapsed_s: float) -> str:
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"""Hook: model-specific speed-of-light % suffix for the prefill log line.
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``batch`` carries the per-request extend/prefix lengths a subclass needs
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for an exact attention pair-count. No model arch here, so returns "";
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Call ``_prefill_attention_pairs(batch)`` for the exact causal
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attention pair-count. No model arch here, so returns "";
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a subclass may override it."""
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return ""
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