[torch.compile] hide slicing under custom op for inductor (vllm-proje…

…ct#8384)

tests/compile/test_full_graph.py

@@ -16,5 +16,7 @@ def test_full_graph(model):
         "The future of AI is",
     ]
     sampling_params = SamplingParams(temperature=0)
-    llm = LLM(model="meta-llama/Meta-Llama-3-8B")
+    llm = LLM(model="meta-llama/Meta-Llama-3-8B",
+              enforce_eager=True,
+              load_format="dummy")
     llm.generate(prompts, sampling_params)

vllm/attention/backends/flash_attn.py

@@ -122,6 +122,40 @@ def _(
     return torch.empty_like(decode_query)
 
 
+@torch.library.custom_op("vllm::reshape_and_cache_flash",
+                         mutates_args=["kv_cache"])
+def reshape_and_cache_flash(
+    key: torch.Tensor,
+    value: torch.Tensor,
+    kv_cache: torch.Tensor,
+    slot_mapping: torch.Tensor,
+    kv_cache_dtype: str,
+    k_scale: float,
+    v_scale: float,
+) -> None:
+    """Inductor cannot deal with inplace operations on views.
+    See https://github.com/pytorch/pytorch/issues/131192
+    and https://github.com/pytorch/pytorch/issues/130174
+    This is a workaround to hide the view operation from the inductor.
+    """
+    return torch.ops._C_cache_ops.reshape_and_cache_flash(
+        key, value, kv_cache[0], kv_cache[1], slot_mapping, kv_cache_dtype,
+        k_scale, v_scale)
+
+
+@reshape_and_cache_flash.register_fake  # type: ignore
+def _(
+    key: torch.Tensor,
+    value: torch.Tensor,
+    kv_cache: torch.Tensor,
+    slot_mapping: torch.Tensor,
+    kv_cache_dtype: str,
+    k_scale: float,
+    v_scale: float,
+) -> None:
+    pass
+
+
 class FlashAttentionBackend(AttentionBackend):
 
     @staticmethod
@@ -653,11 +687,10 @@ def forward(
             # Reshape the input keys and values and store them in the cache.
             # If kv_cache is not provided, the new key and value tensors are
             # not cached. This happens during the initial memory profiling run.
-            ops.reshape_and_cache_flash(
+            torch.ops.vllm.reshape_and_cache_flash(
                 key,
                 value,
-                key_cache,
-                value_cache,
+                kv_cache,
                 attn_metadata.slot_mapping.flatten(),
                 self.kv_cache_dtype,
                 k_scale,
@@ -669,7 +702,6 @@ def forward(
         assert key.shape[0] == num_prefill_tokens + num_decode_tokens
         assert value.shape[0] == num_prefill_tokens + num_decode_tokens
 
-        output = torch.empty_like(query)
         # Query for decode. KV is not needed because it is already cached.
         decode_query = query[num_prefill_tokens:]
         # QKV for prefill.
@@ -680,14 +712,17 @@ def forward(
         assert query.shape[0] == num_prefill_tokens
         assert decode_query.shape[0] == num_decode_tokens
 
+        prefill_output: Optional[torch.Tensor] = None
+        decode_output: Optional[torch.Tensor] = None
+
         if prefill_meta := attn_metadata.prefill_metadata:
             # Prompt run.
             if (kv_cache is None or prefill_meta.block_tables is None
                     or prefill_meta.block_tables.numel() == 0):
                 # normal attention
                 # When block_tables are not filled, it means q and k are the
                 # prompt, and they have the same length.
-                out = torch.ops.vllm.flash_attn_varlen_func(
+                prefill_output = torch.ops.vllm.flash_attn_varlen_func(
                     q=query,
                     k=key,
                     v=value,
@@ -701,42 +736,44 @@ def forward(
                     alibi_slopes=self.alibi_slopes,
                     softcap=self.logits_soft_cap,
                 )
-                assert output[:num_prefill_tokens].shape == out.shape
-                output[:num_prefill_tokens] = out
             else:
                 # prefix-enabled attention
                 assert prefill_meta.seq_lens is not None
                 max_seq_len = max(prefill_meta.seq_lens)
-                output[:
-                       num_prefill_tokens] = torch.ops.vllm.flash_attn_varlen_func(  # noqa
-                           q=query,
-                           k=key_cache,
-                           v=value_cache,
-                           cu_seqlens_q=prefill_meta.query_start_loc,
-                           max_seqlen_q=prefill_meta.max_query_len,
-                           cu_seqlens_k=prefill_meta.seq_start_loc,
-                           max_seqlen_k=max_seq_len,
-                           softmax_scale=self.scale,
-                           causal=True,
-                           alibi_slopes=self.alibi_slopes,
-                           block_table=prefill_meta.block_tables,
-                           softcap=self.logits_soft_cap,
-                       )
-
-        if decode_meta := attn_metadata.decode_metadata:
-            # Decoding run.
-            output[
-                num_prefill_tokens:] = torch.ops.vllm.flash_attn_with_kvcache(
-                    decode_query.unsqueeze(1),
-                    key_cache,
-                    value_cache,
-                    block_table=decode_meta.block_tables,
-                    cache_seqlens=decode_meta.seq_lens_tensor,
+                prefill_output = torch.ops.vllm.flash_attn_varlen_func(  # noqa
+                    q=query,
+                    k=key_cache,
+                    v=value_cache,
+                    cu_seqlens_q=prefill_meta.query_start_loc,
+                    max_seqlen_q=prefill_meta.max_query_len,
+                    cu_seqlens_k=prefill_meta.seq_start_loc,
+                    max_seqlen_k=max_seq_len,
                     softmax_scale=self.scale,
                     causal=True,
                     alibi_slopes=self.alibi_slopes,
+                    block_table=prefill_meta.block_tables,
                     softcap=self.logits_soft_cap,
-                ).squeeze(1)
+                )
 
-        # Reshape the output tensor.
+        if decode_meta := attn_metadata.decode_metadata:
+            # Decoding run.
+            decode_output = torch.ops.vllm.flash_attn_with_kvcache(
+                decode_query.unsqueeze(1),
+                key_cache,
+                value_cache,
+                block_table=decode_meta.block_tables,
+                cache_seqlens=decode_meta.seq_lens_tensor,
+                softmax_scale=self.scale,
+                causal=True,
+                alibi_slopes=self.alibi_slopes,
+                softcap=self.logits_soft_cap,
+            ).squeeze(1)
+
+        if prefill_output is None:
+            assert decode_output is not None
+            return decode_output.view(num_decode_tokens, hidden_size)
+        if decode_output is None:
+            assert prefill_output is not None
+            return prefill_output.view(num_prefill_tokens, hidden_size)
+        output = torch.cat([prefill_output, decode_output], dim=0)
         return output.view(num_tokens, hidden_size)