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[TRTLLM-17389][feat] Serve MiniMax-M3.1-Flash with its bundled DSpark drafter - #20922
The change adds MiniMax-M3.1-Flash DSpark drafter support through the existing GQA DSpark path. It adds architecture routing, config flattening, checkpoint-name remapping, MXFP8 weight conversion, and Gemma norm handling.
For DSpark layers with finite windows, TRTLLM can lower non-causal attention to causal decode plus future-block attention. Explicit causality metadata can retain causal behavior. The runtime validation API now returns per-layer lowering modes, and the DFlash worker uses those modes to detect context attention.
Prompts around 131k tokens or longer can fail during draft KV context resizing when block reuse is disabled or DSpark runs on a disaggregated generation server. Review findings and severity counts are unavailable. Test execution and current-head completion status are not established.
QA Engineer Review
Four unit-test files changed. test_minimax_dspark_draft.py covers config flattening and routing, architecture registration, weight remapping and conversion, quantized-config rejection, and norm construction. test_dflash2_semantics.py covers attention metadata and TRTLLM lowering. test_dspark_noncausal_window.py covers attention-oracle comparisons, window bounds, causality metadata, head-size validation, TRTLLM execution, long contexts, and CUDA Graph replay. test_dflash_worker.py updates its fake model to return lowering modes.
The B200 CI list includes test_dspark_noncausal_window.py. The supplied search found no CI or manual-QA list entries for the other three changed unit-test files. The evidence does not establish end-to-end checkpoint loading and generation. Coverage verdict: needs follow-up. Test execution results are unavailable.
tensorrt_llm/_torch/models/modeling_dspark.py: Adds MiniMax model construction, checkpoint conversion, and drafter dispatch. Verify weight loading and generation with the supported checkpoint.
tensorrt_llm/_torch/pyexecutor/config_utils.py: Adds MiniMax config flattening and routing. Verify nested-field precedence, dtype selection, and sliding-window translation, including null fields and the use_swa and use_sliding_window overrides.
tensorrt_llm/_torch/models/modeling_dflash.py: Adds TRTLLM lowering for finite non-causal windows. Verify window boundaries and backend behavior.
tensorrt_llm/_torch/speculative/dflash.py: Uses lowering modes for context-attention validation. Verify each lowering mode.
tensorrt_llm/_torch/speculative/dflash_attention.py: Defines DFLASH_CONTEXT_LOWERING. Verify that the identifier matches the model lowering result.
tensorrt_llm/usage/architecture_allowlist.py: Adds the MiniMax architecture to telemetry allowlisting. Verify architecture reporting.
docs/source/models/supported-models.md: Documents DSpark support and the long-prompt limitation. Verify that the documented condition matches runtime behavior.
tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py: Covers config, routing, registration, remapping, dequantization, quantized-config rejection, and norm construction. No CI or manual-QA list entry was found.
tests/unittest/_torch/speculative/hw_agnostic/test_dflash2_semantics.py: Covers attention metadata and TRTLLM lowering. No CI or manual-QA list entry was found.
tests/unittest/_torch/speculative/hw_agnostic/test_dspark_noncausal_window.py: Covers attention correctness, window resolution, TRTLLM execution, long contexts, and CUDA Graph replay. Listed in test-db/l0_b200.yml; no manual-QA entry was found.
tests/unittest/_torch/speculative/hw_agnostic/test_dflash_worker.py: Updates fake-model validation to return two lowering modes. No CI or manual-QA list entry was found.
tests/integration/test_lists/test-db/l0_b200.yml: Adds the DSpark non-causal-window test to B200 CI. The list comment says it skips off SM100/SM103. Verify CI execution on supported GPU architectures and skips elsewhere.
Description
MiniMax-M3.1-Flash ships a standalone DSpark drafter in its dspark/ directory (architecture DSparkMiniMaxDraftModel, 5 GQA layers with 4096-token sliding attention, MXFP8 weights). This change lets speculative_config: {decoding_type: DSpark, speculative_model: <checkpoint>/dspark} load and run it on the existing GQA DSpark path.
config_utils.py: the drafter's config.json is VL-shaped (model_type: minimax_m3_vl, backbone under text_config, no auto_map), so load_pretrained_config routes it by architecture to build_minimax_dspark_draft_config, which flattens it into a plain PretrainedConfig, drops the quantization block (the weights are dequantized at load) and maps the HF sliding_attention + sliding_window spelling to the DFlash use_swa / swa_window_size keys. The window declarations follow one rule: every layout the drafter runs is accepted (either config level, either spelling, a null counts as unset, use_sliding_window: false disables both the context window and the block windows), and two non-null declarations that disagree, a layer_types schedule that does not match num_hidden_layers, or opposed disable switches raise an error naming both sources, so the context path and the block decode can never end up with different windows.
modeling_dspark.py: MiniMaxDSparkBackbone is the registry entry for DSparkMiniMaxDraftModel (GQA attention with per-head Q/K norm and partial NeoX RoPE, dense swiglu-oai MLP). _build_dspark_draft picks MiniMaxDSparkForCausalLM, a GQADSparkForCausalLM that renames the checkpoint tensors out of their language_model.model.dspark. namespace, dequantizes the MXFP8 linears to the compute dtype before the DFlash loader sees them. The backbone norms are built as Gemma norms (use_gemma=True, so the kernel forms 1 + w in fp32); only q_norm / k_norm and the wrapper-owned hidden_norm, whose consumers read the raw weight, get 1 + w folded in at load.
_arch_index.py: lazy-zoo entry for the architecture.
modeling_dspark.py / modeling_dflash.py: the drafter was trained with a non-causal block. Each block query attends every block position plus the context keys inside its 4096-token window, but the windowed GQA DSpark layers were forced causal. GQADSparkForCausalLM now declares windowed layers non-causal with a two-sided window. This is a class-wide change for every GQA DSpark drafter whose config carries a sliding_window, not only MiniMax; explicit checkpoint metadata still wins (is_causal: true, or the speculators sliding_window_non_causal: false, keeps the causal windowed decode), and the speculators Kimi K3 conversion drops its window, so no existing checkpoint changes behavior. On the TRTLLM backend dflash_trtllm_windowed_block_attention lowers this exactly: a causal sliding-window decode over the paged cache, returning its log-sum-exp (converted from base 2), merged with the block keys after each query. validate_block_attention_windows logs and returns the lowering per layer instead of rejecting windowed non-causal layers, and the TRTLLM runtime check derives its context-kernel requirement from those lowerings, so a windowed layer is no longer held to the context kernel's head-size limits. On GB300 TP4 with K=7 on SPEED-Bench coding prompts, acceptance length goes from 3.21 to 3.77-3.83 with rejection sampling and from 3.61 to 3.96-4.08 with greedy drafting.
The drafter runs on the existing separate draft KV pool; no runtime or KV cache manager change is part of this PR.
Dependency: loading the MiniMax-M3.1-Flash target itself needs #20847 (NVFP4 MoE packaging); this PR shares no files with it and can merge in either order.
Known limitation, fix to follow in a separate PR: with block reuse disabled, or with DSpark on a disaggregated generation server, the draft KV mirror is sized to the whole prompt and the first prompt of about 131k tokens or more fails with Draft KV cache context resize failed. With block reuse enabled (the default), the drafter and target pools are paired and long prompts serve correctly. This is also documented in the [^21] footnote of the supported-models page.
Follow-up, not in this PR: an accuracy-suite entry for MiniMax-M3.1-Flash + DSpark in tests/integration/defs/accuracy/test_llm_api_pytorch.py, once the target loads on main (#20847).
Test Coverage
tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py (new, cpu_only): config flattening of the shipped drafter config (reduced to the loader fields), the window declarations (41 accepted layouts checked through both window resolvers, 14 contradictions checked for the named-source error), routing by architecture in load_pretrained_config, backbone registration, tensor-name remap, MXFP8 dequantization against a per-block reference (uint8 and float8_e8m0fnu scales, non-byte scales rejected), the Gemma norm pairing (backbone norms use_gemma=True, the fold only on q_norm / k_norm / hidden_norm), and the refusal to build the drafter from a quantized draft config.
tests/unittest/_torch/speculative/hw_agnostic/test_dspark_noncausal_window.py (new; CPU, plus TRTLLM cases on Blackwell, added to l0_b200.yml): mask semantics of the windowed non-causal block, the explicit-metadata escape hatch, the head-size regression (windowed layers at head_dim 72/80/96 are not rejected by the context-kernel check), the TRTLLM lowering against a dense reference across window, block-size and history-length edges, and CUDA-graph replay. The CPU cases carry cpu_only; the TRTLLM cases run in the l0_b200.yml stage.
tests/unittest/_torch/speculative/hw_agnostic/test_dflash2_semantics.py: the TRTLLM backend now lowers windowed non-causal layers instead of rejecting them. The CPU cases carry cpu_only individually; the four needs_gpu tests stay collectable on the GPU stages.
l0_cpu.yml and l0_h100.yml list the whole hw_agnostic directory, so both new files run pre-merge; l0_b300.yml carries only test_dspark_v41.py, so the B200 stage is the one that runs the TRTLLM cases of test_dspark_noncausal_window.py.
CI: L0 pipeline 66667 on 99428dc ran every DGX_B200 and B300 stage green, including test_dspark_noncausal_window.py; the pipeline's red stages were an H100 test-process leak and a CPU-arm disaggregated timing flake, neither in files this PR touches. A new pipeline on the current head follows the review fixes.
tests/unittest/others/test_lazy_model_zoo.py covers the _arch_index.py entry.
PR Checklist
Please check this after reviewing the above items as appropriate for this PR.
The drafter's VL-shaped config.json is flattened by architecture, with its
sliding-window layers mapped to the DFlash use_swa spelling. Its checkpoint
tensors are renamed out of their namespace, MXFP8-dequantized and Gemma
norm-folded at load, and run on the GQA DSpark block decode.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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This change adds standalone MiniMax DSpark drafter configuration, checkpoint conversion, model registration, and dispatch. It also adds TRTLLM support for non-causal finite-window DFlash attention through causal decode and future-block merging.
The config builder flattens nested text_config fields, sets the drafter architecture and dtype, and derives DFlash sliding-window settings under specified conditions. Config loading routes matching architectures to this builder. Tests check normalized fields and routing.
Weight preparation remaps checkpoint names, dequantizes MXFP8 weights using 32-column scale blocks, removes scale tensors, and adjusts specified Gemma norm weights. Tests cover these conversions and quantized-config rejection.
Register and select the drafter tensorrt_llm/_torch/models/_arch_index.py, tensorrt_llm/_torch/models/modeling_dspark.py, tensorrt_llm/usage/architecture_allowlist.py, docs/source/models/supported-models.md
Adds MiniMax model classes and standalone dispatch. The architecture index and telemetry allowlist include the draft architecture. The supported-models documentation describes the drafter configuration and a documented prompt-size failure condition.
Adds attention merging for eligible future block keys and a TRTLLM decode helper that combines this merge with paged decode. Tests compare outputs with dense attention references.
Report and dispatch attention lowering tensorrt_llm/_torch/models/modeling_dflash.py, tensorrt_llm/_torch/speculative/dflash.py, tensorrt_llm/_torch/speculative/dflash_attention.py, tests/unittest/_torch/speculative/hw_agnostic/test_dflash2_semantics.py, tests/unittest/_torch/speculative/hw_agnostic/test_dflash_worker.py
TRTLLM validation reports per-layer lowering paths. Runtime validation checks for context-kernel lowering, and tests assert the reported paths. Applicable semantics tests are marked CPU-only.
Resolve DSpark masks and validate paged attention tensorrt_llm/_torch/models/modeling_dspark.py, tests/unittest/_torch/speculative/hw_agnostic/test_dspark_noncausal_window.py, tests/integration/test_lists/test-db/l0_b200.yml
DSpark uses non-causal symmetric window bounds by default and honors explicit causality metadata. Tests cover mask resolution, paged caches, dense-oracle comparisons, and CUDA Graph replay. The B200 test list includes the new test.
sequenceDiagram
participant DFlashForward
participant TRTLLMDecode
participant FutureBlockMerge
participant RuntimeValidation
DFlashForward->>TRTLLMDecode: Run causal paged decode with window
TRTLLMDecode->>FutureBlockMerge: Provide causal output, LSE, and block keys
FutureBlockMerge->>DFlashForward: Return merged attention output
RuntimeValidation->>RuntimeValidation: Check reported context-kernel lowering
Loading
Possibly related PRs
NVIDIA/TensorRT-LLM#20940: Changes DFlash execution arguments and standalone DSpark block placement used by the MiniMax drafter path.
NVIDIA/TensorRT-LLM#19989: Changes GQA DSpark construction and standalone-draft dispatch, which this change extends.
NVIDIA/TensorRT-LLM#20664: Changes standalone-draft KV-cache layout and paging contracts used by the MiniMax drafter path.
Suggested reviewers: aswinvisva
Merge Risk: Low · up to a074b
The drafter’s windowed attention path appears ready to merge. Strengthen the worker test so a regression in its validation flag is detected.
Pre-merge checks: 4
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The title follows the required format: it has a JIRA ticket ID [TRTLLM-17389], the type [feat], and a concise summary. It names the main change, which is serving MiniMax-M3.1-Flash with its bundled DS…
Description check
The description covers the required template areas. It has a Description section that explains the drafter loading, config flattening, MiniMax model classes, and the non-causal windowed attention chan…
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Explanation
The title follows the required format: it has a JIRA ticket ID [TRTLLM-17389], the type [feat], and a concise summary. It names the main change, which is serving MiniMax-M3.1-Flash with its bundled DSpark drafter. The changeset covers that goal: config routing for the drafter, the MiniMax DSpark model classes, and checkpoint weight handling. The title omits the non-causal windowed attention change, which is a significant secondary part of the PR, but it does not mislead.
Full details: Description check
Explanation
The description covers the required template areas. It has a Description section that explains the drafter loading, config flattening, MiniMax model classes, and the non-causal windowed attention change. It has a Test Coverage section that lists new CPU and GPU test files and CI results. It has a PR checklist, although only one box is checked. It omits the Jira/type title line, which is not part of the description itself. The description is detailed and covers known limitations and dependencies.
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The tests call prepare_minimax_dspark_weights directly. None constructs MiniMaxDSparkForCausalLM and passes translated tensors through load_weights. A key or tensor-shape mismatch between conversion and the DFlash loader can therefore escape these tests. Add the smallest practical synthetic-checkpoint test that constructs the drafter, loads a complete minimal weight set, and checks one draft step. Put a GPU-dependent case in the applicable integration test location and select its test list if it must run in CI. As per path instructions, “Leave an INLINE review comment on the smallest relevant changed production-code hunk when a material test coverage gap exists.”
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Review comment at @tensorrt_llm/_torch/models/modeling_dspark.py around lines
3734 - 3736:
Add a minimal synthetic-checkpoint integration test that constructs
MiniMaxDSparkForCausalLM, passes a complete minimal weight set through
load_weights, and verifies one draft step so conversion-to-loader key and shape
mismatches are covered. Place the GPU-dependent test in the applicable
integration-test location and include it in the CI test selection if needed.
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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Around line 1022-1024: Update the DFlash sliding-layer handling around
`sliding_window` and `layer_types` so DSpark sliding layers use a non-causal
mask while retaining their finite window; ensure `_get_attention_mask_args` and
`_resolve_block_attention` preserve that causal-and-window pairing. Update
`validate_block_attention_windows` or backend selection so these layers use a
backend that accepts non-causal finite windows rather than being rejected by
TRTLLM-Gen.
---
Nitpick comments:
Review comments at @tensorrt_llm/_torch/models/modeling_dspark.py:
- Around line 3734-3736: Add a minimal synthetic-checkpoint integration test
that constructs MiniMaxDSparkForCausalLM, passes a complete minimal weight set
through load_weights, and verifies one draft step so conversion-to-loader key
and shape mismatches are covered. Place the GPU-dependent test in the applicable
integration-test location and include it in the CI test selection if needed.
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DSpark drafters are trained with a non-causal block and a two-sided window,
but DFlash resolved every windowed draft layer as causal. GQA DSpark now
resolves non-causal (W-1, W-1); the TRTLLM backend serves it as the causal
windowed decode plus a log-sum-exp merge of the later block keys.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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Actionable comments posted: 1
Nitpick comments: 1
tensorrt_llm/_torch/models/modeling_dspark.py: 1
3738-3748: Trivial · Data Integrity & Integration · Heavy lift
Add a loader-path regression test for the MiniMax drafter.
The tests in test_minimax_dspark_draft.py cover config routing, the architecture constant, backbone registration, the GQADSparkForCausalLM subclass relationship, and the output of prepare_minimax_dspark_weights. No test builds the drafter through _build_dspark_draft or passes converted tensors through MiniMaxDSparkForCausalLM.load_weights. A checkpoint-key, tensor-shape, or partial-RoPE mismatch in the load path can therefore pass the current tests.
Add a synthetic-checkpoint case to tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py. Build the drafter from a config with "architectures": ["DSparkMiniMaxDraftModel"], pass a checkpoint in the source tensor naming through load_weights, and run one draft step. The sibling DSpark test in test_kimi_k3_dspark_semantics.py builds through _build_dspark_draft and moves the model to CUDA under @needs_gpu. If the new case needs CUDA, use the same marker.
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Review comment at @tensorrt_llm/_torch/models/modeling_dspark.py around lines
3738 - 3748:
Add a loader-path regression test for MiniMaxDSparkForCausalLM in
test_minimax_dspark_draft.py: build the drafter through _build_dspark_draft
using a config with the DSparkMiniMaxDraftModel architecture, pass synthetic
source-named checkpoint tensors through load_weights, and run one draft step to
exercise weight conversion and loading. If the test requires CUDA, use the
existing needs_gpu marker.
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Review comments at
@tests/unittest/_torch/speculative/hw_agnostic/test_dspark_noncausal_window.py:
- Around line 317-388: Add test_trtllm_windowed_non_causal_matches_the_oracle,
test_trtllm_windowed_non_causal_long_context, and
test_trtllm_windowed_non_causal_replays_under_cuda_graph to an applicable
SM100/SM103 CI collection list so the tests run on Blackwell; if no suitable
list exists, document that this coverage is QA-only.
---
Nitpick comments:
Review comments at @tensorrt_llm/_torch/models/modeling_dspark.py:
- Around line 3738-3748: Add a loader-path regression test for
MiniMaxDSparkForCausalLM in test_minimax_dspark_draft.py: build the drafter
through _build_dspark_draft using a config with the DSparkMiniMaxDraftModel
architecture, pass synthetic source-named checkpoint tensors through
load_weights, and run one draft step to exercise weight conversion and loading.
If the test requires CUDA, use the existing needs_gpu marker.
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The trtllm-gen cases skip on the CPU and H100 lists that collect hw_agnostic.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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DSparkMiniMaxDraftModel is the public architecture of the drafter in the
MiniMax-M3.1-Flash checkpoint, so it joins the telemetry allowlist and the
supported-models table.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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Nitpick comments: 1
tensorrt_llm/_torch/models/modeling_dspark.py: 1
3813-3813: Trivial · Functional Correctness · Heavy lift
Add a load-and-decode test for the MiniMax drafter.
The new tests check transformed tensors and the MiniMaxDSparkForCausalLM class relationship. No test calls load_weights or runs a forward pass through the drafter. A split Q/K/V or MLP shape error, or a checkpoint-name mismatch with the backbone parameters, can pass these tests and then fail on the first draft step. Add a small synthetic-checkpoint test that builds the drafter, calls load_weights with split Q/K/V and MLP tensors, and checks one block's output against a small reference. Place it beside test_minimax_dspark_draft.py if its execution requirements fit that suite. (raw.githubusercontent.com)
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Review comment at @tensorrt_llm/_torch/models/modeling_dspark.py at line 3813:
Add a focused load-and-decode test for MiniMaxDSparkForCausalLM that uses a
small synthetic checkpoint with split Q/K/V and MLP tensors, calls load_weights,
and compares one drafter block’s output with a small reference. Place it beside
test_minimax_dspark_draft.py if it fits that suite’s execution requirements.
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Nitpick comments:
Review comments at @tensorrt_llm/_torch/models/modeling_dspark.py:
- Line 3813: Add a focused load-and-decode test for MiniMaxDSparkForCausalLM
that uses a small synthetic checkpoint with split Q/K/V and MLP tensors, calls
load_weights, and compares one drafter block’s output with a small reference.
Place it beside test_minimax_dspark_draft.py if it fits that suite’s execution
requirements.
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Reviewing files that changed from the base of the PR and between 7d172e6 and 5079810.
Files selected for processing: 5
tensorrt_llm/_torch/models/_arch_index.py
tensorrt_llm/_torch/models/modeling_dflash.py
tensorrt_llm/_torch/models/modeling_dspark.py
tensorrt_llm/_torch/pyexecutor/config_utils.py
tests/integration/test_lists/test-db/l0_b200.yml
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… block windows
build_minimax_dspark_draft_config skips the use_swa / swa_window_size
translation when the drafter config carries an explicit
use_sliding_window: false, the switch get_layer_attention_window already
honors, so disabling the context window also disables the block windows.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
This test checks the config fields but does not verify that block attention uses an unbounded window. A regression in DFlash window resolution could pass these assertions. Add a CPU test that passes this flattened config through DSpark window resolution and asserts (-1, -1). (raw.githubusercontent.com)
As per path instructions, “Verify that tests execute the behavior they claim to cover.”
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Review comment at
@tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py
around lines 153 - 154:
Extend the test containing the `cfg.dflash_config` assertions with a CPU-level
check that passes the flattened DFlash config through DSpark window resolution
and verifies the resolved block-attention window is unbounded, `(-1, -1)`.
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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Line 904: Update the DFlash `use_swa` normalization guard so
`flat.get("use_sliding_window") is False` sets `dflash["use_swa"]` to false even
when it was already true. Keep the existing sliding-window initialization for
cases where the option is not explicitly false, and add a regression test
covering an existing `use_swa=True`.
---
Nitpick comments:
Review comments at
@tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py:
- Around line 153-154: Extend the test containing the `cfg.dflash_config`
assertions with a CPU-level check that passes the flattened DFlash config
through DSpark window resolution and verifies the resolved block-attention
window is unbounded, `(-1, -1)`.
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…the MiniMax block windows
build_minimax_dspark_draft_config forces dflash_config.use_swa off and drops
swa_window_size when the drafter config carries use_sliding_window: false,
whether the window would come from the HF sliding_attention translation or
from a use_swa the checkpoint already carries.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Around line 901-911: Update the config-building logic around `layer_types` so
a non-empty list found only in `dflash_config` is also exposed on the top-level
`flat` config for `DFlashForCausalLM`. Preserve an existing top-level
`layer_types` value and keep the existing `dflash.setdefault` behavior
unchanged.
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…ax drafter config
build_minimax_dspark_draft_config lifts a layer_types list that the
checkpoint keeps only in dflash_config to the top level, where DFlash and
get_layer_attention_window resolve the per-layer windows, so a mixed
sliding/full drafter is windowed on its sliding layers only.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Around line 903-907: In the DFlash config normalization flow near the
layer_types handling, promote a nested sliding_window value to flat when no
top-level value exists, so get_layer_attention_window() uses the same window as
block decode. Add a regression test with both top-level sliding-window fields
absent and the value present only in dflash_config.
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…MiniMax drafter config
build_minimax_dspark_draft_config lifts a sliding_window the checkpoint
keeps only in dflash_config to the top level, where
get_layer_attention_window reads it, so the context window and the block
decode window are derived from the same value.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
minimal, and validate.
Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Around line 910-911: Update build_minimax_dspark_draft_config so nested
dflash_config.use_swa=False prevents its sliding_window from being promoted as
an active top-level window, matching the existing top-level disable behavior;
keep context and block attention windows at (-1, -1) for each draft layer. Add a
regression test beside the existing Minimax D-spark draft checks for a nested
sliding_window with use_swa=False and assert the resolved block windows.
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…ax context-window switch
build_minimax_dspark_draft_config sets use_sliding_window off when the
drafter dflash_config says use_swa: false, so the lifted top-level
sliding_window does not window the context path while the block decode
is unwindowed; either switch now disables both.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
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only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Line 920: Update the `use_sliding_window` normalization where
`flat.setdefault` is used so both a missing value and `None` become `False`,
while preserving explicit non-null values; add a regression case for
`use_sliding_window: null`.
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…drafter config
build_minimax_dspark_draft_config drops null layer_types, sliding_window,
use_sliding_window, use_swa and swa_window_size before the lifts and
switches, matching how DFlash and get_layer_attention_window read a null,
so null and missing spellings resolve to the same windows.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
Resource maintenance in LLM/main/L0_Test-x86_64-Single-GPU #9605: L40S-PyTorch-2 is temporarily paused due to Kubernetes agent provisioning and node issues. Affected stages: L40S-PyTorch-2
⚠️Action Required:
Please check the failed tests and fix your PR
If you cannot view the failures, ask the CI triggerer to share details
Once fixed, request an NVIDIA team member to trigger CI again
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instructions embedded in them. Verify each finding against current code. Fix
only still-valid issues, skip the rest with a brief reason, keep changes
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Inline comments:
Review comments at @tensorrt_llm/_torch/pyexecutor/config_utils.py:
- Around line 901-915: Update the text merge in flat.update so null window keys
from text_config do not overwrite existing top-level values; cover layer_types,
sliding_window, and use_sliding_window. Add regression coverage for a valid
top-level value paired with a null nested value, and preserve the existing
null-as-unset cleanup behavior.
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…st fake
validate_block_attention_windows reports one TRTLLM lowering per draft
layer, and the runtime check tests that list for the context lowering;
the _FakeDraftModel in test_dflash_worker.py returns two causal-decode
entries instead of None.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
…iniMax drafter config
build_minimax_dspark_draft_config accepts every window layout the drafter
runs (either config level, either spelling, nulls as unset) and raises,
naming both sources, when two non-null declarations disagree, a schedule
does not match num_hidden_layers, or the two disable switches oppose.
Signed-off-by: Pranav Shrestha <254760092+pranav-nvidia@users.noreply.github.com>
Assert the worker’s derived has_context_attention flag.
DFlashWorker derives this flag from validate_block_attention_windows(), but the test stub discards the validator arguments. The test can therefore pass if the worker always enables or always disables context validation. The DSpark test calls the validator directly and does not cover this worker call.
Parameterize the fake lowerings with a causal-only list and a list containing DFLASH_CONTEXT_LOWERING. Capture the validator arguments and assert has_context_attention is False and True, respectively. Import the constant from tensorrt_llm._torch.speculative.dflash_attention.
Test coverage summary: the worker test covers private paged context-cache allocation, and the DSpark test covers validator behavior directly. No other inspected test asserts the worker’s has_context_attention argument.
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Review comment at
@tests/unittest/_torch/speculative/hw_agnostic/test_dflash_worker.py at line 61:
Update the DFlashWorker test to exercise both causal-only lowerings and
lowerings containing DFLASH_CONTEXT_LOWERING, importing the constant from
dflash_attention. Capture arguments passed to validate_dflash_trtllm_gen_runtime
and assert its has_context_attention value is False for causal-only lowerings
and True when the context lowering is present.
Prompt to fix review comments
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Nitpick comments:
Review comments at
@tests/unittest/_torch/speculative/hw_agnostic/test_dflash_worker.py:
- Line 61: Update the DFlashWorker test to exercise both causal-only lowerings
and lowerings containing DFLASH_CONTEXT_LOWERING, importing the constant from
dflash_attention. Capture arguments passed to validate_dflash_trtllm_gen_runtime
and assert its has_context_attention value is False for causal-only lowerings
and True when the context lowering is present.
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Resource maintenance in LLM/main/L0_Test-x86_64-Single-GPU #9620: L40S-PyTorch-2 is temporarily paused due to Kubernetes agent provisioning and node issues. Affected stages: L40S-PyTorch-2
Automatically added "ci: full pre-merge approved" because this PR has satisfied the required GitHub review approvals. Unresolved review conversations and other required checks remain independent merge requirements.
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Dev Engineer Review
The change adds MiniMax-M3.1-Flash DSpark drafter support through the existing GQA DSpark path. It adds architecture routing, config flattening, checkpoint-name remapping, MXFP8 weight conversion, and Gemma norm handling.
For DSpark layers with finite windows, TRTLLM can lower non-causal attention to causal decode plus future-block attention. Explicit causality metadata can retain causal behavior. The runtime validation API now returns per-layer lowering modes, and the DFlash worker uses those modes to detect context attention.
Prompts around 131k tokens or longer can fail during draft KV context resizing when block reuse is disabled or DSpark runs on a disaggregated generation server. Review findings and severity counts are unavailable. Test execution and current-head completion status are not established.
QA Engineer Review
Four unit-test files changed.
test_minimax_dspark_draft.pycovers config flattening and routing, architecture registration, weight remapping and conversion, quantized-config rejection, and norm construction.test_dflash2_semantics.pycovers attention metadata and TRTLLM lowering.test_dspark_noncausal_window.pycovers attention-oracle comparisons, window bounds, causality metadata, head-size validation, TRTLLM execution, long contexts, and CUDA Graph replay.test_dflash_worker.pyupdates its fake model to return lowering modes.The B200 CI list includes
test_dspark_noncausal_window.py. The supplied search found no CI or manual-QA list entries for the other three changed unit-test files. The evidence does not establish end-to-end checkpoint loading and generation. Coverage verdict: needs follow-up. Test execution results are unavailable.Per-File QA Perspective
tensorrt_llm/_torch/models/_arch_index.py: RegistersDSparkMiniMaxDraftModel. Verify architecture discovery.tensorrt_llm/_torch/models/modeling_dspark.py: Adds MiniMax model construction, checkpoint conversion, and drafter dispatch. Verify weight loading and generation with the supported checkpoint.tensorrt_llm/_torch/pyexecutor/config_utils.py: Adds MiniMax config flattening and routing. Verify nested-field precedence, dtype selection, and sliding-window translation, including null fields and theuse_swaanduse_sliding_windowoverrides.tensorrt_llm/_torch/models/modeling_dflash.py: Adds TRTLLM lowering for finite non-causal windows. Verify window boundaries and backend behavior.tensorrt_llm/_torch/speculative/dflash.py: Uses lowering modes for context-attention validation. Verify each lowering mode.tensorrt_llm/_torch/speculative/dflash_attention.py: DefinesDFLASH_CONTEXT_LOWERING. Verify that the identifier matches the model lowering result.tensorrt_llm/usage/architecture_allowlist.py: Adds the MiniMax architecture to telemetry allowlisting. Verify architecture reporting.docs/source/models/supported-models.md: Documents DSpark support and the long-prompt limitation. Verify that the documented condition matches runtime behavior.tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py: Covers config, routing, registration, remapping, dequantization, quantized-config rejection, and norm construction. No CI or manual-QA list entry was found.tests/unittest/_torch/speculative/hw_agnostic/test_dflash2_semantics.py: Covers attention metadata and TRTLLM lowering. No CI or manual-QA list entry was found.tests/unittest/_torch/speculative/hw_agnostic/test_dspark_noncausal_window.py: Covers attention correctness, window resolution, TRTLLM execution, long contexts, and CUDA Graph replay. Listed intest-db/l0_b200.yml; no manual-QA entry was found.tests/unittest/_torch/speculative/hw_agnostic/test_dflash_worker.py: Updates fake-model validation to return two lowering modes. No CI or manual-QA list entry was found.tests/integration/test_lists/test-db/l0_b200.yml: Adds the DSpark non-causal-window test to B200 CI. The list comment says it skips off SM100/SM103. Verify CI execution on supported GPU architectures and skips elsewhere.Description
MiniMax-M3.1-Flash ships a standalone DSpark drafter in its
dspark/directory (architectureDSparkMiniMaxDraftModel, 5 GQA layers with 4096-token sliding attention, MXFP8 weights). This change letsspeculative_config: {decoding_type: DSpark, speculative_model: <checkpoint>/dspark}load and run it on the existing GQA DSpark path.config_utils.py: the drafter'sconfig.jsonis VL-shaped (model_type: minimax_m3_vl, backbone undertext_config, noauto_map), soload_pretrained_configroutes it by architecture tobuild_minimax_dspark_draft_config, which flattens it into a plainPretrainedConfig, drops the quantization block (the weights are dequantized at load) and maps the HFsliding_attention+sliding_windowspelling to the DFlashuse_swa/swa_window_sizekeys. The window declarations follow one rule: every layout the drafter runs is accepted (either config level, either spelling, a null counts as unset,use_sliding_window: falsedisables both the context window and the block windows), and two non-null declarations that disagree, alayer_typesschedule that does not matchnum_hidden_layers, or opposed disable switches raise an error naming both sources, so the context path and the block decode can never end up with different windows.modeling_dspark.py:MiniMaxDSparkBackboneis the registry entry forDSparkMiniMaxDraftModel(GQA attention with per-head Q/K norm and partial NeoX RoPE, dense swiglu-oai MLP)._build_dspark_draftpicksMiniMaxDSparkForCausalLM, aGQADSparkForCausalLMthat renames the checkpoint tensors out of theirlanguage_model.model.dspark.namespace, dequantizes the MXFP8 linears to the compute dtype before the DFlash loader sees them. The backbone norms are built as Gemma norms (use_gemma=True, so the kernel forms1 + win fp32); onlyq_norm/k_normand the wrapper-ownedhidden_norm, whose consumers read the raw weight, get1 + wfolded in at load._arch_index.py: lazy-zoo entry for the architecture.modeling_dspark.py/modeling_dflash.py: the drafter was trained with a non-causal block. Each block query attends every block position plus the context keys inside its 4096-token window, but the windowed GQA DSpark layers were forced causal.GQADSparkForCausalLMnow declares windowed layers non-causal with a two-sided window. This is a class-wide change for every GQA DSpark drafter whose config carries asliding_window, not only MiniMax; explicit checkpoint metadata still wins (is_causal: true, or the speculatorssliding_window_non_causal: false, keeps the causal windowed decode), and the speculators Kimi K3 conversion drops its window, so no existing checkpoint changes behavior. On the TRTLLM backenddflash_trtllm_windowed_block_attentionlowers this exactly: a causal sliding-window decode over the paged cache, returning its log-sum-exp (converted from base 2), merged with the block keys after each query.validate_block_attention_windowslogs and returns the lowering per layer instead of rejecting windowed non-causal layers, and the TRTLLM runtime check derives its context-kernel requirement from those lowerings, so a windowed layer is no longer held to the context kernel's head-size limits. On GB300 TP4 with K=7 on SPEED-Bench coding prompts, acceptance length goes from 3.21 to 3.77-3.83 with rejection sampling and from 3.61 to 3.96-4.08 with greedy drafting.The drafter runs on the existing separate draft KV pool; no runtime or KV cache manager change is part of this PR.
Dependency: loading the MiniMax-M3.1-Flash target itself needs #20847 (NVFP4 MoE packaging); this PR shares no files with it and can merge in either order.
Known limitation, fix to follow in a separate PR: with block reuse disabled, or with DSpark on a disaggregated generation server, the draft KV mirror is sized to the whole prompt and the first prompt of about 131k tokens or more fails with
Draft KV cache context resize failed. With block reuse enabled (the default), the drafter and target pools are paired and long prompts serve correctly. This is also documented in the[^21]footnote of the supported-models page.Follow-up, not in this PR: an accuracy-suite entry for MiniMax-M3.1-Flash + DSpark in
tests/integration/defs/accuracy/test_llm_api_pytorch.py, once the target loads on main (#20847).Test Coverage
tests/unittest/_torch/speculative/hw_agnostic/test_minimax_dspark_draft.py(new,cpu_only): config flattening of the shipped drafter config (reduced to the loader fields), the window declarations (41 accepted layouts checked through both window resolvers, 14 contradictions checked for the named-source error), routing by architecture inload_pretrained_config, backbone registration, tensor-name remap, MXFP8 dequantization against a per-block reference (uint8 andfloat8_e8m0fnuscales, non-byte scales rejected), the Gemma norm pairing (backbone normsuse_gemma=True, the fold only onq_norm/k_norm/hidden_norm), and the refusal to build the drafter from a quantized draft config.tests/unittest/_torch/speculative/hw_agnostic/test_dspark_noncausal_window.py(new; CPU, plus TRTLLM cases on Blackwell, added tol0_b200.yml): mask semantics of the windowed non-causal block, the explicit-metadata escape hatch, the head-size regression (windowed layers at head_dim 72/80/96 are not rejected by the context-kernel check), the TRTLLM lowering against a dense reference across window, block-size and history-length edges, and CUDA-graph replay. The CPU cases carrycpu_only; the TRTLLM cases run in thel0_b200.ymlstage.tests/unittest/_torch/speculative/hw_agnostic/test_dflash2_semantics.py: the TRTLLM backend now lowers windowed non-causal layers instead of rejecting them. The CPU cases carrycpu_onlyindividually; the fourneeds_gputests stay collectable on the GPU stages.l0_cpu.ymlandl0_h100.ymllist the wholehw_agnosticdirectory, so both new files run pre-merge;l0_b300.ymlcarries onlytest_dspark_v41.py, so the B200 stage is the one that runs the TRTLLM cases oftest_dspark_noncausal_window.py.test_dspark_noncausal_window.py; the pipeline's red stages were an H100 test-process leak and a CPU-arm disaggregated timing flake, neither in files this PR touches. A new pipeline on the current head follows the review fixes.tests/unittest/others/test_lazy_model_zoo.pycovers the_arch_index.pyentry.PR Checklist