[BugFix] Add support for MTP num_speculative_tokens > 1 with sparse MLA (#34552)
Signed-off-by: Lucas Wilkinson <lwilkins@redhat.com> Signed-off-by: Matthew Bonanni <mbonanni@redhat.com> Co-authored-by: Matthew Bonanni <mbonanni@redhat.com>
This commit is contained in:
Lucas Wilkinson
@@ -12,6 +12,7 @@ from vllm.utils.deep_gemm import (
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get_paged_mqa_logits_metadata,
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is_deep_gemm_supported,
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)
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from vllm.utils.math_utils import cdiv
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from vllm.utils.platform_utils import num_compute_units
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from vllm.v1.attention.backend import (
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AttentionBackend,
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@@ -24,6 +25,7 @@ from vllm.v1.attention.backends.utils import (
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split_decodes_and_prefills,
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split_prefill_chunks,
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)
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from vllm.v1.worker.cp_utils import get_total_cp_world_size
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logger = init_logger(__name__)
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@@ -214,20 +216,39 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
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if self.vllm_config.speculative_config
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else 0
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)
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if self.num_speculative_tokens > 1:
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raise ValueError(
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"Sparse MLA only supports "
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"num_speculative_tokens <= 1 because the DeepGEMM "
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"fp8_paged_mqa_logits kernel does not support next_n > 2. "
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f"Got num_speculative_tokens={self.num_speculative_tokens}."
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)
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self.reorder_batch_threshold += self.num_speculative_tokens
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sm_count = num_compute_units(self.device.index)
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self.num_sms = sm_count
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self.decode_lens_buffer = torch.empty(
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(scheduler_config.max_num_seqs,), dtype=torch.int32, device=self.device
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(scheduler_config.max_num_batched_tokens,),
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dtype=torch.int32,
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device=self.device,
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)
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# Pre-allocated buffers for flattening (spec decode).
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self.arange_buffer = torch.arange(
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scheduler_config.max_num_seqs * (1 + self.num_speculative_tokens),
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dtype=torch.int32,
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device=self.device,
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)
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self.expanded_seq_lens_buffer = torch.zeros(
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(scheduler_config.max_num_batched_tokens,),
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dtype=torch.int32,
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device=self.device,
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)
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max_num_blocks_per_req = cdiv(
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self.vllm_config.model_config.max_model_len,
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self.kv_cache_spec.block_size * get_total_cp_world_size(),
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)
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self.expanded_block_table_buffer = torch.zeros(
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(
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scheduler_config.max_num_batched_tokens,
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max_num_blocks_per_req,
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),
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dtype=torch.int32,
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device=self.device,
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)
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# See: DeepGMM/csrc/apis/attention.hpp
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@@ -326,42 +347,97 @@ class DeepseekV32IndexerMetadataBuilder(AttentionMetadataBuilder):
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common_attn_metadata.query_start_loc_cpu[: num_decodes + 1]
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)
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# Use CPU to avoid GPU sync; breaking async scheduling
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requires_padding = (decode_lens_cpu.max() > decode_lens_cpu.min()).item()
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# Decide which top-k kernel to use based on batch size and sequence length
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batch_size = num_decodes
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_is_large_context = common_attn_metadata.max_seq_len > 8192
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# Decision logic based on micro-benchmark results:
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# - large_context_topk wins for batch <= 128 and seq_len > 8K
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# - top_k_per_row_decode wins for batch > 128 or seq_len <= 8K
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use_large_context_topk = batch_size <= 128 and _is_large_context
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next_n = 1 + self.num_speculative_tokens
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if next_n > 1:
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offsets = torch.arange(next_n, device=self.device, dtype=torch.int32)
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else:
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offsets = None
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seq_lens = common_attn_metadata.seq_lens[:num_decodes]
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# DeepGEMM is required for the paged MQA logits on CUDA devices
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if current_platform.is_cuda() and is_deep_gemm_supported():
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self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
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seq_lens, self.kv_cache_spec.block_size, self.num_sms
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)
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block_table = common_attn_metadata.block_table_tensor[:num_decodes, ...]
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# Padded CUDA graph requests have block_table entries of -1.
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# Clamp to 0 to prevent OOB access in the DeepGEMM kernel.
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# This is safe because padded requests have seq_lens=0, so the
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# kernel produces no meaningful output for those rows.
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block_table.clamp_(min=0)
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max_decode_len = int(decode_lens_cpu.max().item())
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if max_decode_len > 1:
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# Flatten multi-token decode requests into single-token
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# batch entries, expanding seq_lens and block tables so
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# the kernel always sees next_n=1.
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# Assume 4 requests with seq_lens [10, 7, 12, 0] (the final req is
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# padding) and decode_lens [3, 1, 4, 0] in the below example comments.
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# The context lengths are therefore
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# [10-3, 7-1, 12-4, 0-0] = [7, 6, 8, 0].
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# 3 + 1 + 4 + 0 = 8
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actual_expanded = int(decode_lens_cpu.sum().item())
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# [7, 6, 8, 0] -> [7, 7, 7, 6, 8, 8, 8, 8]
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expanded_base = torch.repeat_interleave(
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seq_lens - decode_lens, decode_lens
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)
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# [0, 3, 4, 8] -> [0, 0, 0, 3, 4, 4, 4, 4]
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expanded_starts = torch.repeat_interleave(
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common_attn_metadata.query_start_loc[:num_decodes], decode_lens
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)
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# [0, 1, 2, 0, 0, 1, 2, 3]
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positions_within = (
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self.arange_buffer[:actual_expanded] - expanded_starts
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)
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# [8, 9, 10, 7, 9, 10, 11, 12, ...] where ... is unused buffer space
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self.expanded_seq_lens_buffer[:actual_expanded] = (
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expanded_base + positions_within + 1
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)
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self.expanded_seq_lens_buffer[actual_expanded:] = 0
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seq_lens = self.expanded_seq_lens_buffer[:num_decode_tokens]
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# Give each of the flattened entries the same block table row as the
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# original request.
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self.expanded_block_table_buffer[:actual_expanded] = (
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torch.repeat_interleave(block_table, decode_lens, dim=0)
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)
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if actual_expanded < num_decode_tokens:
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self.expanded_block_table_buffer[
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actual_expanded:num_decode_tokens, 0
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] = 0
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block_table = self.expanded_block_table_buffer[:num_decode_tokens]
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# All reqs now have decode_len=1
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self.decode_lens_buffer[:num_decode_tokens] = 1
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decode_lens = self.decode_lens_buffer[:num_decode_tokens]
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offsets = None
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batch_size = num_decode_tokens
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else:
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next_n = 1 + self.num_speculative_tokens
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if next_n > 1:
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offsets = torch.arange(
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next_n, device=self.device, dtype=torch.int32
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)
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else:
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offsets = None
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batch_size = num_decodes
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# DeepGEMM is required for the paged MQA logits on CUDA devices
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if current_platform.is_cuda() and is_deep_gemm_supported():
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self.scheduler_metadata_buffer[:] = get_paged_mqa_logits_metadata(
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seq_lens,
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self.kv_cache_spec.block_size,
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self.num_sms,
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)
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# Decide which top-k kernel to use based on batch size and sequence length
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# Decision logic based on micro-benchmark results:
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# - large_context_topk wins for batch <= 128 and seq_len > 8K
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# - top_k_per_row_decode wins for batch > 128 or seq_len <= 8K
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_is_large_context = common_attn_metadata.max_seq_len > 8192
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use_large_context_topk = batch_size <= 128 and _is_large_context
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decode_metadata = DeepSeekV32IndexerDecodeMetadata(
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block_table=block_table,
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seq_lens=common_attn_metadata.seq_lens[:num_decodes],
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seq_lens=seq_lens,
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decode_lens=decode_lens,
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requires_padding=requires_padding,
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requires_padding=False,
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schedule_metadata=self.scheduler_metadata_buffer,
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use_large_context_topk=use_large_context_topk,
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offsets=offsets,
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