uvm_cache_stats for direct mapped (pytorch#1952)

Summary:
Pull Request resolved: pytorch#1952

- Implement python frontend for the previous diff (split for backward compatibility)
- Revise the existing benchmark to use the uvm_cache_stats for stats instead of cache_miss_counter.
- Implement unit test for uvm_cache_stats for direct mapped.

Differential Revision: D48439568

fbshipit-source-id: a1f75d886f5b013ec04f074f25fdbfdd6b9ccb87

fbgemm_gpu/bench/split_table_batched_embeddings_benchmark.py

@@ -1727,6 +1727,7 @@ def nbit_uvm(
 @click.option("--fp8-exponent-bits", type=int, default=None)
 @click.option("--fp8-exponent-bias", type=int, default=None)
 @click.option("--record-cache", is_flag=True, default=False)
+@click.option("--uvm-host-mapped", is_flag=True, default=False)
 @click.option(
     "--dump-requests", type=int, default=0, help="number of reqs to dump (0=no dump)"
 )
@@ -1753,6 +1754,7 @@ def nbit_uvm_compare_direct_mapped(
     fp8_exponent_bits: Optional[int],
     fp8_exponent_bias: Optional[int],
     record_cache: bool,
+    uvm_host_mapped: bool,
     dump_requests: int,
 ) -> None:
     logging.info(json.dumps({k: str(v) for k, v in locals().items()}, indent=2))
@@ -1837,18 +1839,21 @@ def bench_uvm_cls(
             enforce_hbm=enforce_hbm,
             fp8_exponent_bits=fp8_exponent_bits,
             fp8_exponent_bias=fp8_exponent_bias,
-            record_cache_metrics=RecordCacheMetrics(record_cache, record_cache),
+            gather_uvm_cache_stats=record_cache,
+            uvm_host_mapped=uvm_host_mapped,
         ).cuda()
         emb.fill_random_weights()
 
-        # label nvtx only when cache counter is off
-        nvtx_range = "" if record_cache else f"UVM-{name.upper()}"
-        callback_after_warmup = emb.reset_cache_miss_counter if record_cache else None
-        requests = requests_uvm[:1] if record_cache else requests_uvm
+        nvtx_range = (
+            f"UVM-RECORD-CACHE-{name.upper()}"
+            if record_cache
+            else f"UVM-{name.upper()}"
+        )
+        callback_after_warmup = emb.reset_uvm_cache_stats if record_cache else None
 
         torch.cuda.cudart().cudaProfilerStart()
         time_per_iter = benchmark_requests(
-            requests,
+            requests_uvm,
             lambda indices, offsets, per_sample_weights: emb.forward(
                 indices.int(),
                 offsets.int(),
@@ -1881,12 +1886,14 @@ def bench_uvm_cls(
             )
 
         if record_cache:
-            cmc = emb.cache_miss_counter.detach().cpu().numpy().tolist()
+            ucs = emb.uvm_cache_stats.detach().cpu().numpy().tolist()
             cache_stats = {
-                "miss_forward_count": cmc[0],
-                "unique_miss": cmc[1],
-                "unique_req": cmc[2],
-                "nondedup_req": cmc[3],
+                "num_calls": ucs[0],
+                "num_requested_indices": ucs[1],
+                "num_unique_indices": ucs[2],
+                "num_unique_misses": ucs[3],
+                "num_conflict_unique_misses": ucs[4],
+                "num_conflict_misses": ucs[5],
             }
             stats[name]["cache_stats"] = cache_stats
             logging.info(f"[{name:>8s}] cache stats {cache_stats}")
@@ -1932,6 +1939,7 @@ def bench_uvm_cls(
 @click.option("--batch-size", default=512)
 @click.option("--cache-algorithm", default="lru")
 @click.option("--cache-load-factor", default=0.2)
+@click.option("--cache-assoc", default=32)
 @click.option("--embedding-dim", default=128)
 @click.option("--weights-precision", type=SparseType, default=SparseType.INT4)
 @click.option("--iters", default=100)
@@ -1954,6 +1962,7 @@ def nbit_cache(  # noqa C901
     batch_size: int,
     cache_algorithm: str,
     cache_load_factor: float,
+    cache_assoc: int,
     embedding_dim: int,
     weights_precision: SparseType,
     iters: int,
@@ -2003,6 +2012,7 @@ def nbit_cache(  # noqa C901
         enforce_hbm=enforce_hbm,
         fp8_exponent_bits=fp8_exponent_bits,
         fp8_exponent_bias=fp8_exponent_bias,
+        cache_assoc=cache_assoc,
     ).cuda()
     emb_nc.fill_random_weights()
 
@@ -2027,6 +2037,7 @@ def nbit_cache(  # noqa C901
         enforce_hbm=enforce_hbm,
         fp8_exponent_bits=fp8_exponent_bits,
         fp8_exponent_bias=fp8_exponent_bias,
+        cache_assoc=cache_assoc,
     ).cuda()
     emb.fill_random_weights()
 

fbgemm_gpu/fbgemm_gpu/split_table_batched_embeddings_ops_inference.py

@@ -584,13 +584,7 @@ def prefetch_32way(self, linear_cache_indices: Tensor) -> None:
             )
         )
         if self.gather_uvm_cache_stats:
-            # Accumulate local_uvm_cache_stats (int32) into uvm_cache_stats (int64).
-            # We may wanna do this accumulation atomically, but as it's only for monitoring,
-            # slightly inaccurate result may be acceptable.
-            self.uvm_cache_stats = torch.add(
-                self.uvm_cache_stats, self.local_uvm_cache_stats
-            )
-            self.local_uvm_cache_stats.zero_()
+            self._accumulate_uvm_cache_stats()
 
     def prefetch_1way(self, linear_cache_indices: Tensor) -> None:
         if self.cache_algorithm == CacheAlgorithm.LRU:
@@ -608,6 +602,9 @@ def prefetch_1way(self, linear_cache_indices: Tensor) -> None:
                 self.timestep_counter.get(),
                 self.lxu_state,
                 self.lxu_cache_miss_timestamp,
+                16,  # row_alignment; using default value.
+                self.gather_uvm_cache_stats,
+                self.local_uvm_cache_stats,
             )
         else:
             raise ValueError("Direct Mapped for LRU only")
@@ -620,8 +617,21 @@ def prefetch_1way(self, linear_cache_indices: Tensor) -> None:
                 linear_cache_indices,
                 self.lxu_cache_state,
                 self.total_cache_hash_size,
+                self.gather_uvm_cache_stats,
+                self.local_uvm_cache_stats,
             )
         )
+        if self.gather_uvm_cache_stats:
+            self._accumulate_uvm_cache_stats()
+
+    def _accumulate_uvm_cache_stats(self) -> None:
+        # Accumulate local_uvm_cache_stats (int32) into uvm_cache_stats (int64).
+        # We may wanna do this accumulation atomically, but as it's only for monitoring,
+        # slightly inaccurate result may be acceptable.
+        self.uvm_cache_stats = torch.add(
+            self.uvm_cache_stats, self.local_uvm_cache_stats
+        )
+        self.local_uvm_cache_stats.zero_()
 
     def _update_cache_miss_counter(
         self,

fbgemm_gpu/test/split_table_batched_embeddings_test.py

@@ -5575,6 +5575,129 @@ def test_nbit_uvm_cache_stats(self, N: int, dtype: SparseType) -> None:
                 self.assertEqual(num_conflict_miss, e[1])
                 cc1.reset_uvm_cache_stats()
 
+    @unittest.skipIf(*gpu_unavailable)
+    @given(
+        N=st.integers(min_value=1, max_value=8),
+        dtype=st.sampled_from([SparseType.INT8, SparseType.INT4, SparseType.INT2]),
+    )
+    @settings(verbosity=Verbosity.verbose, max_examples=MAX_EXAMPLES, deadline=None)
+    def test_nbit_direct_mapped_uvm_cache_stats(
+        self, N: int, dtype: SparseType
+    ) -> None:
+        # Create an abstract split table
+        D = 8
+        T = 2
+        E = 10**3
+        Ds = [D] * T
+        Es = [E] * T
+        cc = IntNBitTableBatchedEmbeddingBagsCodegen(
+            embedding_specs=[
+                (
+                    "",
+                    E,
+                    D,
+                    dtype,
+                    EmbeddingLocation.MANAGED_CACHING,
+                )
+                for (E, D) in zip(Es, Ds)
+            ],
+            device=torch.cuda.current_device(),
+            gather_uvm_cache_stats=True,
+            cache_assoc=1,  # Direct Mapped
+        )
+        cc.fill_random_weights()
+
+        # Create fake input data and the target output
+        x1 = torch.Tensor([[[1], [1]], [[3], [4]]]).cuda()
+        x2 = torch.Tensor([[[2], [1]], [[3], [4]]]).cuda()
+        x3 = torch.Tensor([[[5], [6]], [[7], [8]]]).cuda()
+
+        xs = [x1, x2, x3]
+        # num_unique_indices, num_unique_misses
+        # note that these are cumulative over calls; and also "unique" is per batch.
+        target_counter_list = [[3, 3], [4, 4], [4, 8]]
+        num_calls_expected = 0
+        num_indices_expcted = 0
+        num_unique_indices_expected = 0
+        for x, t_counter in zip(xs, target_counter_list):
+            (indices, offsets) = get_table_batched_offsets_from_dense(x, use_cpu=False)
+            for _ in range(N):
+                num_calls_expected = num_calls_expected + 1
+                num_indices_expcted = num_indices_expcted + len(indices)
+                cc(indices.int(), offsets.int())
+                (
+                    num_calls,
+                    num_indices,
+                    num_unique_indices,
+                    num_unique_misses,
+                    num_conflict_unique_miss,
+                    num_conflict_miss,
+                ) = cc.get_uvm_cache_stats().cpu()
+                # Note num_unique_indices is cumulative stats.
+                num_unique_indices_expected = num_unique_indices_expected + t_counter[0]
+                self.assertEqual(num_calls, num_calls_expected)
+                self.assertEqual(num_indices, num_indices_expcted)
+                self.assertEqual(num_unique_indices, 0)  # N/A for Direct Mapped
+                self.assertEqual(num_unique_misses, 0)  # N/A for Direct Mapped
+                self.assertEqual(
+                    num_conflict_unique_miss, t_counter[1]
+                )  # number of actually inserted rows for Direct Mapped
+                self.assertEqual(num_conflict_miss, 0)
+
+        T = 1  # for simplicity
+        Ds = [D] * T
+        Es = [E] * T
+        cc1 = IntNBitTableBatchedEmbeddingBagsCodegen(
+            embedding_specs=[
+                (
+                    "",
+                    E,
+                    D,
+                    SparseType.INT8,
+                    EmbeddingLocation.MANAGED_CACHING,
+                )
+                for (E, D) in zip(Es, Ds)
+            ],
+            device=torch.cuda.current_device(),
+            gather_uvm_cache_stats=True,
+            cache_sets=1,  # Only one set.
+            cache_assoc=1,  # Direct Mapped
+        )
+        cc1.fill_random_weights()
+
+        associativty = 1  # Direct-Mapped
+        repetition = 17
+        indices1 = torch.Tensor(
+            [[list(range(0, associativty))] * repetition]
+        ).cuda()  # no conflict miss
+        indices2 = torch.Tensor(
+            [[list(range(0, associativty + 1))] * repetition]
+        ).cuda()  # 1 * 17 conflict miss per request
+        indices3 = torch.Tensor(
+            [[list(range(0, associativty + 10))] * repetition]
+        ).cuda()  # 10 * 17 conflict misses per request
+
+        # num_conflict_unique_miss, num_conflict_miss
+        expected = [[1, 0], [1, 17], [1, 170]]
+
+        accum_num_conflict_miss = 0
+        for x, e in zip((indices1, indices2, indices3), expected):
+            (indices, offsets) = get_table_batched_offsets_from_dense(x, use_cpu=False)
+            for _ in range(N):
+                cc1(indices.int(), offsets.int())
+                (
+                    _,
+                    _,
+                    _,
+                    _,
+                    num_conflict_unique_miss,
+                    num_conflict_miss,
+                ) = cc1.get_uvm_cache_stats().cpu()
+                # for DM this represents number of actually inserted rows
+                self.assertEqual(num_conflict_unique_miss, e[0])
+                accum_num_conflict_miss += e[1]
+                self.assertEqual(num_conflict_miss, accum_num_conflict_miss)
+
     @given(
         T=st.integers(min_value=1, max_value=64),
         B=st.integers(min_value=1, max_value=64),