[#10931][feat] AutoDeploy: one-model spec dec#11701
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@lucaslie , @govind-ramnarayan : is this ready to be reviewed? |
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📝 WalkthroughWalkthroughIntroduces a new Changes
Estimated code review effort🎯 4 (Complex) | ⏱️ ~60 minutes 🚥 Pre-merge checks | ✅ 1 | ❌ 2❌ Failed checks (2 warnings)
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Actionable comments posted: 15
🧹 Nitpick comments (7)
tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_switch_to_generate_inplace.py (1)
20-20: Use module-level import instead of importingSequenceInfodirectly.Line 20 imports a class symbol directly, which conflicts with the repository’s Python import rule.
Proposed refactor
import torch -from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import SequenceInfo +from tensorrt_llm._torch.auto_deploy.custom_ops import attention_interface @@ -def _make_seq_info(extra_activate=()) -> SequenceInfo: +def _make_seq_info(extra_activate=()) -> attention_interface.SequenceInfo: @@ - si = SequenceInfo( + si = attention_interface.SequenceInfo( @@ -def _nest_prefill(si: SequenceInfo, input_ids, pages_per_seq, cache_loc, **kw): +def _nest_prefill(si: attention_interface.SequenceInfo, input_ids, pages_per_seq, cache_loc, **kw):As per coding guidelines, "
**/*.py: Python imports must use formfrom package.subpackage import module(neverfrom module import Class)."🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_switch_to_generate_inplace.py` at line 20, Replace the direct class import of SequenceInfo with a module-level import to follow the repo import rule: change the import to bring in the attention_interface module (e.g., import tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface as attention_interface) and then update all usages of SequenceInfo in this test (function/class names in this file) to reference attention_interface.SequenceInfo; ensure only the import line and references are modified so behavior remains unchanged.tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py (1)
274-278: Run the resize probe forward in inference mode.This path is used only for memory sizing; wrapping the forward call in
torch.inference_mode()avoids autograd allocations that can inflatemem_reserved_for_forwardand reduce KV-cache capacity estimates.♻️ Suggested patch
cm.info.set_max_num_tokens_sample() try: # TODO (lucaslie): revisit this logic as part of spec dec cudagraph support... - if getattr(mod, "_requires_csi", False): - mod(cache_seq_interface=cm) - else: - mod(**cm.named_args) + with torch.inference_mode(): + if getattr(mod, "_requires_csi", False): + mod(cache_seq_interface=cm) + else: + mod(**cm.named_args)🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py` around lines 274 - 278, The forward probe used for memory sizing should run under torch.inference_mode() to avoid autograd allocations: wrap the calls to mod(...) in a torch.inference_mode() context manager so both branches (when getattr(mod, "_requires_csi", False) calls mod(cache_seq_interface=cm) and the else branch calling mod(**cm.named_args)) execute inside torch.inference_mode(), preserving existing argument usage and behavior while preventing grad-related memory allocations during the resize probe.tensorrt_llm/_torch/auto_deploy/custom_ops/attention/flashinfer_attention.py (1)
309-310: Avoid unused unpacked token count in host metadata prep.Line 310 unpacks
num_prefill_tokensbut never uses it (RUF059).💡 Proposed fix
- num_prefill, num_prefill_tokens, num_decode = batch_info.get_absorbed_info() + num_prefill, _num_prefill_tokens, num_decode = batch_info.get_absorbed_info()🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tensorrt_llm/_torch/auto_deploy/custom_ops/attention/flashinfer_attention.py` around lines 309 - 310, The host metadata unpack currently assigns an unused variable num_prefill_tokens from BatchInfo.get_absorbed_info(); update the unpack to avoid the unused symbol — either ignore the middle value by assigning it to _ or only unpack the two needed values (e.g., assign num_prefill and num_decode) from BatchInfo(batch_info_host).get_absorbed_info() so num_prefill_tokens is not created but the rest of the code (using BatchInfo and get_absorbed_info) continues to work.tensorrt_llm/_torch/auto_deploy/custom_ops/attention/torch_backend_attention.py (1)
320-322: Clean up unused unpacked value from absorbed metadata.Line 321 unpacks
num_prefill_tokensbut doesn’t use it (RUF059).💡 Proposed fix
- num_prefill, num_prefill_tokens, num_decode = batch_info.get_absorbed_info() + num_prefill, _num_prefill_tokens, num_decode = batch_info.get_absorbed_info()🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tensorrt_llm/_torch/auto_deploy/custom_ops/attention/torch_backend_attention.py` around lines 320 - 322, The code unpacks three values from BatchInfo.get_absorbed_info() but never uses num_prefill_tokens; update the unpack to remove the unused variable (e.g., unpack only num_prefill and num_decode) or replace num_prefill_tokens with an underscore to indicate it’s intentionally ignored in the BatchInfo / get_absorbed_info call site where batch_info_host is converted and num_seq is computed from num_prefill + num_decode.tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_causal_conv.py (1)
192-194: Drop unused unpacked metadata to keep lint clean.At Line 193,
num_prefill_tokensis unpacked but unused (RUF059).💡 Proposed fix
- num_prefill, num_prefill_tokens, num_decode = batch_info.get_absorbed_info() + num_prefill, _num_prefill_tokens, num_decode = batch_info.get_absorbed_info()🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_causal_conv.py` around lines 192 - 194, The unpacked variable num_prefill_tokens from BatchInfo(batch_info_host).get_absorbed_info() is unused; change the unpacking in the block that creates BatchInfo and calls get_absorbed_info() so the unused metadata is discarded (e.g., replace num_prefill_tokens with a throwaway name like _ or otherwise only extract needed elements) and keep the subsequent computation of num_seq using num_prefill and num_decode unchanged; locate this in the code around the BatchInfo constructor call and the get_absorbed_info() unpacking.tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py (1)
423-423: Avoid unpacking an unused value.Line 423 unpacks
num_decode_tokensbut never uses it, which triggers Ruff (RUF059).💡 Proposed fix
- num_prefill_tokens, num_extend_tokens, num_decode_tokens = self.get_num_tokens() + num_prefill_tokens, num_extend_tokens, _num_decode_tokens = self.get_num_tokens()🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py` at line 423, The code unpacks three values from self.get_num_tokens() but never uses num_decode_tokens, causing a linter warning; change the unpack to only capture the needed values (e.g., num_prefill_tokens, num_extend_tokens = self.get_num_tokens()) or replace the third target with a discard variable (e.g., _ or _num_decode_tokens) where the call appears (the call to self.get_num_tokens() in attention_interface.py), and ensure any downstream code that expects num_decode_tokens is updated accordingly.tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_gather_logits_before_lm_head.py (1)
25-25: Prefer module-namespace import forBatchInfoLine 25 imports a class symbol directly. The repository guideline asks for importing module namespaces and using
module.Symbol.♻️ Example refactor pattern
-from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import BatchInfo +from tensorrt_llm._torch.auto_deploy.custom_ops import attention_interface - batch_info = BatchInfo() + batch_info = attention_interface.BatchInfo()As per coding guidelines "Python imports must use form
from package.subpackage import module(neverfrom module import Class)".🤖 Prompt for AI Agents
Verify each finding against the current code and only fix it if needed. In `@tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_gather_logits_before_lm_head.py` at line 25, The test imports the class BatchInfo directly; change the import to the module form (import the module namespace) so usages use module.BatchInfo instead. Replace the line that currently does "from tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import BatchInfo" with a module import (e.g., "from tensorrt_llm._torch.auto_deploy.custom_ops import attention_interface") and update all references in test_gather_logits_before_lm_head.py to use attention_interface.BatchInfo wherever BatchInfo is referenced.
🤖 Prompt for all review comments with AI agents
Verify each finding against the current code and only fix it if needed.
Inline comments:
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py`:
- Around line 411-413: The update method (update(self, batch_info: List[int]) ->
None) currently assigns batch_info into self._batch_info_np[:6] without
validating length, causing obscure numpy broadcast errors for legacy 3-value
inputs; add an explicit check that len(batch_info) == 6 at the top of update and
raise a clear ValueError (e.g. "batch_info must be length 6: [fields...]") when
it isn't, so callers get an immediate, descriptive error rather than a
silent/broadcast failure; keep the assignment to self._batch_info_np[:6] after
the check.
- Around line 1362-1375: The code overwrites cu_seqlen before computing
extraction_indices, so extraction_indices uses the reset arange rather than each
sequence's true end; to fix, read or save the original cu_seqlen (via
self.get_arg or a local variable) before you create/overwrite the new cu_seqlen,
then compute extraction_indices = (original_cu_seqlen[1:] - 1).long() and use
that when calling self.copy_ for input_ids; keep references to cu_seqlen,
input_ids_flat, extraction_indices, self.get_arg and self.copy_ to locate the
change.
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/attention/trtllm_attention.py`:
- Around line 286-289: The BatchInfo initialization calls .numpy() which breaks
under torch.compile FakeTensor tracing; inside the register_fake implementation
replace the direct BatchInfo(batch_info_host) call with a FakeTensor guard:
detect FakeTensor instances (e.g. isinstance(batch_info_host,
torch._subclasses.fake_tensor.FakeTensor) or similar project convention) and
when fake, compute max_blocks_per_seq and max_batch_size from
batch_info_host.shape/metadata directly instead of constructing BatchInfo;
otherwise keep using BatchInfo(batch_info_host) and then call
get_max_blocks_per_seq() and get_max_batch_size() as before (refer to symbols
BatchInfo, register_fake, get_max_blocks_per_seq, get_max_batch_size).
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/mamba_backend_common.py`:
- Line 54: The unpacking on the call to batch_info.get_absorbed_info() creates
unused variables num_prefill_tokens and num_decode causing a lint error; change
the unpacking in the metadata preparation to only capture the used value (e.g.,
assign to num_prefill or use _ placeholders) so that only the necessary variable
from get_absorbed_info() is bound (refer to batch_info.get_absorbed_info and the
num_prefill usage to locate the edit).
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_mamba.py`:
- Around line 163-164: The tuple returned from
BatchInfo(batch_info_host).get_absorbed_info() is unpacked into num_prefill,
num_prefill_tokens, num_decode but num_prefill_tokens is never used; update the
unpacking in torch_backend_mamba.py to drop the unused binding (e.g., unpack
into num_prefill, num_decode or use a throwaway name like _ for the middle
element) so only the used symbols (num_prefill and num_decode) remain.
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/mla/flashinfer_mla.py`:
- Around line 439-440: The unpacked value num_prefill_tokens from
BatchInfo(batch_info_host).get_absorbed_info() is unused and triggers RUF059;
update the unpacking in the function containing this call so the unused slot is
intentionally marked (e.g., replace num_prefill_tokens with a throwaway name
like _ or _num_prefill_tokens) to indicate it’s intentionally unused and satisfy
the linter while keeping num_prefill and num_decode as-is.
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/mla/torch_backend_mla.py`:
- Around line 368-369: The unpacked value num_prefill_tokens from
BatchInfo.get_absorbed_info() is unused; update the unpack to either ignore it
or rename it to indicate intentional unusedness (e.g., use "_" or
"_num_prefill_tokens") so the RUF059 warning is resolved—locate the assignment
to batch_info = BatchInfo(batch_info_host) and the subsequent unpack of
get_absorbed_info() and change "num_prefill, num_prefill_tokens, num_decode =
batch_info.get_absorbed_info()" to "num_prefill, _, num_decode =
batch_info.get_absorbed_info()" (or "num_prefill, _num_prefill_tokens,
num_decode") to make the unused value explicit.
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/utils/block_table_ragged.py`:
- Around line 95-97: The custom Triton kernel adjust_block_table_triton writes
into extra_idx via tl.store(extra_idx_ptr + seq_id, removed) but extra_idx is
not listed in mutates_args, breaking functionalization; update the kernel
registration/call site for adjust_block_table_triton to include extra_idx in
mutates_args (or the equivalent mutating-args list) so PyTorch knows extra_idx
is mutated by the op, ensuring graph correctness and preserving the tl.store
side-effect referencing extra_idx_ptr/extra_idx.
In `@tensorrt_llm/_torch/auto_deploy/llm_args.py`:
- Around line 173-178: Replace the loose Dict[str, Any] on the LlmArgs field
speculative_model_kwargs with a concrete Pydantic model (e.g.,
SpeculativeModelConfig) that enumerates the supported keys (model_name, device,
batch_size, temperature, max_tokens, any other allowed options) and their
types/defaults; declare that dataclass/Model in the same module (or an adjacent
models module), update the Field(...) type on LlmArgs.speculative_model_kwargs
to use SpeculativeModelConfig with default_factory=SpeculativeModelConfig and
keep/adjust the description, and update any code that accessed keys as dicts to
read attributes from SpeculativeModelConfig (and update imports or tests
accordingly) so runtime/schema validation and generated OpenAPI/JSON schema
reflect the concrete fields.
In `@tensorrt_llm/_torch/auto_deploy/models/custom/modeling_eagle.py`:
- Around line 746-747: The wrapper currently assigns target_logits =
csi.info.maybe_gather_and_squeeze(out.logits) for local verification and then
returns that gathered tensor as the wrapper's logits, but
ADEngine._run_forward() will gather again and double-corrupt shapes/content;
update the wrapper so you only use maybe_gather_and_squeeze(out.logits) for
local checking (use a separate variable like gathered_for_check) and ensure the
wrapper output's logits field returns the raw, ungathered tensor (out.logits)
instead of target_logits so ADEngine._run_forward() receives the original
logits; adjust references around target_logits and the return at the code that
constructs the wrapper output (the place returning logits at the end of the
forward wrapper) accordingly.
- Around line 705-716: The code currently filters kwargs with
name.endswith("hidden_states_cache"), missing keys like "hidden_states_cache_1"
and causing empty buffers or wrong ordering; update the filter to match any key
that starts with "hidden_states_cache" (e.g.,
name.startswith("hidden_states_cache")) and change the sort key to order capture
layers deterministically by numeric suffix (use a small regex to extract
trailing digits from the buffer name and sort by that integer, falling back to
the full name or 0 when no digits are present). Ensure you import re if needed
and keep the subsequent concatenation (hidden_states =
torch.cat([buf[:num_tokens] for _, buf in buffers], dim=1)) intact.
In `@tensorrt_llm/_torch/auto_deploy/models/eagle.py`:
- Around line 142-145: The assert currently passes multi-element weight tensors
(e.g., n_embed from sub_gm.graph.get_attr(f"{embed_name}.weight")) into
torch._assert which triggers an "ambiguous bool" runtime error; change the
condition to a scalar boolean by testing the tensor's element count (e.g., use
n_embed.numel() > 0 or n_embed.numel() != 0) and pass that scalar boolean to
torch._assert along with the same message, and apply the same fix to the other
five places where a weight tensor from get_attr() is passed directly into
torch._assert.
In `@tests/integration/test_lists/test-db/l0_h100.yml`:
- Around line 442-443: The one-model Eagle3 test has been commented out,
removing CI coverage for the feature; either re-enable the test case
accuracy/test_llm_api_autodeploy.py::TestLlama3_1_8B_Instruct_Eagle3::test_eagle3_one_model
by undoing the commented-out entry in the YAML and ensuring it passes CI, or
move it to a tracked quarantine entry: add the test to an explicit quarantine
list with a linked issue ID and clear re-enable criteria (what needs to be fixed
and target re-enable date/condition) so CI visibility and traceability are
preserved.
In
`@tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/attention/test_attention_op.py`:
- Around line 57-67: BatchInfo.update(...) currently uses ng for both
generate-related fields which can misrepresent decode token count in edge cases
(e.g., max_seq_len=0); compute the actual decode token count used by the test
(e.g., decode_tokens = int(num_decode_tokens.item()) if a Tensor else
num_decode_tokens or otherwise derive it from the test's generate/seq_len logic)
and pass that value into BatchInfo.update([nc, npt, 0, 0, ng, decode_tokens])
instead of using ng for the final element so BatchInfo reflects the real decode
token count; update the local variable computation near ng and then call
BatchInfo.update with the new decode_tokens variable before serializing
batch_info_host.
In
`@tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_switch_to_generate_inplace.py`:
- Around line 165-190: The helper _setup_decode_batch currently accepts an
unused parameter swc_vals; remove this dead parameter from the
_setup_decode_batch signature and from every call site (e.g.,
test_decode_increment_by_one and other tests that pass swc_vals) and delete any
leftover references or comments about swc_vals so the helper and tests only pass
and accept the actual used arguments (positions, pages_per_seq, cache_loc,
etc.). Ensure the signature and all call sites are updated consistently to avoid
mismatches.
---
Nitpick comments:
In `@tensorrt_llm/_torch/auto_deploy/custom_ops/attention_interface.py`:
- Line 423: The code unpacks three values from self.get_num_tokens() but never
uses num_decode_tokens, causing a linter warning; change the unpack to only
capture the needed values (e.g., num_prefill_tokens, num_extend_tokens =
self.get_num_tokens()) or replace the third target with a discard variable
(e.g., _ or _num_decode_tokens) where the call appears (the call to
self.get_num_tokens() in attention_interface.py), and ensure any downstream code
that expects num_decode_tokens is updated accordingly.
In
`@tensorrt_llm/_torch/auto_deploy/custom_ops/attention/flashinfer_attention.py`:
- Around line 309-310: The host metadata unpack currently assigns an unused
variable num_prefill_tokens from BatchInfo.get_absorbed_info(); update the
unpack to avoid the unused symbol — either ignore the middle value by assigning
it to _ or only unpack the two needed values (e.g., assign num_prefill and
num_decode) from BatchInfo(batch_info_host).get_absorbed_info() so
num_prefill_tokens is not created but the rest of the code (using BatchInfo and
get_absorbed_info) continues to work.
In
`@tensorrt_llm/_torch/auto_deploy/custom_ops/attention/torch_backend_attention.py`:
- Around line 320-322: The code unpacks three values from
BatchInfo.get_absorbed_info() but never uses num_prefill_tokens; update the
unpack to remove the unused variable (e.g., unpack only num_prefill and
num_decode) or replace num_prefill_tokens with an underscore to indicate it’s
intentionally ignored in the BatchInfo / get_absorbed_info call site where
batch_info_host is converted and num_seq is computed from num_prefill +
num_decode.
In
`@tensorrt_llm/_torch/auto_deploy/custom_ops/mamba/torch_backend_causal_conv.py`:
- Around line 192-194: The unpacked variable num_prefill_tokens from
BatchInfo(batch_info_host).get_absorbed_info() is unused; change the unpacking
in the block that creates BatchInfo and calls get_absorbed_info() so the unused
metadata is discarded (e.g., replace num_prefill_tokens with a throwaway name
like _ or otherwise only extract needed elements) and keep the subsequent
computation of num_seq using num_prefill and num_decode unchanged; locate this
in the code around the BatchInfo constructor call and the get_absorbed_info()
unpacking.
In `@tensorrt_llm/_torch/auto_deploy/transform/library/kvcache.py`:
- Around line 274-278: The forward probe used for memory sizing should run under
torch.inference_mode() to avoid autograd allocations: wrap the calls to mod(...)
in a torch.inference_mode() context manager so both branches (when getattr(mod,
"_requires_csi", False) calls mod(cache_seq_interface=cm) and the else branch
calling mod(**cm.named_args)) execute inside torch.inference_mode(), preserving
existing argument usage and behavior while preventing grad-related memory
allocations during the resize probe.
In
`@tests/unittest/_torch/auto_deploy/unit/singlegpu/custom_ops/test_switch_to_generate_inplace.py`:
- Line 20: Replace the direct class import of SequenceInfo with a module-level
import to follow the repo import rule: change the import to bring in the
attention_interface module (e.g., import
tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface as
attention_interface) and then update all usages of SequenceInfo in this test
(function/class names in this file) to reference
attention_interface.SequenceInfo; ensure only the import line and references are
modified so behavior remains unchanged.
In
`@tests/unittest/_torch/auto_deploy/unit/singlegpu/transformations/library/test_gather_logits_before_lm_head.py`:
- Line 25: The test imports the class BatchInfo directly; change the import to
the module form (import the module namespace) so usages use module.BatchInfo
instead. Replace the line that currently does "from
tensorrt_llm._torch.auto_deploy.custom_ops.attention_interface import BatchInfo"
with a module import (e.g., "from tensorrt_llm._torch.auto_deploy.custom_ops
import attention_interface") and update all references in
test_gather_logits_before_lm_head.py to use attention_interface.BatchInfo
wherever BatchInfo is referenced.
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govind-ramnarayan
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govind-ramnarayan
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/bot run --extra-stage "DGX_B200-4_GPUs-AutoDeploy-1, DGX_H100-4_GPUs-AutoDeploy-1" --disable-fail-fast |
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/bot run --extra-stage "DGX_B200-4_GPUs-AutoDeploy-1, DGX_H100-4_GPUs-AutoDeploy-1" --disable-fail-fast |
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PR_Github #39131 [ run ] triggered by Bot. Commit: |
xinhe-nv
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Co-authored-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com> Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com>
…ix low acceptance rates Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com>
Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
…but it now defaults to False unnest_sequences() now uses batch_info.is_gather_required() to automatically match how tokens were gathered in nest_sequences(), making it a true inverse. Also fix test_engine to pass gather_context_logits=True so the full-sequence reference logits comparison remains valid. Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
Three categories of failures addressed:
1. Pydantic type check (test_all_fields_have_allowed_types):
Add speculative_model_kwargs to AutoDeployLlmArgs compatibility
exempt list. It uses Dict[str, Any] for the same reason as
model_kwargs: arbitrary HF config overrides for the draft model.
2. KV cache / delta rule tensor shape mismatches:
The new unnest_sequences() branch assumed gather_context_logits=False
(the new default) means the model output is pre-gathered, but the
three affected tests don't include gather_logits_before_lm_head so
their output is still [total_tokens, hidden]. Fix by:
- Restoring the is_gather_required() branch in unnest_sequences()
with a clear docstring explaining both modes
- Explicitly passing gather_context_logits=True in the nest_sequences()
calls in test_kv_cache.py, test_gated_delta_rule_cache.py, and
test_torch_gated_delta_rule_cache.py
3. EagleWrapper resource_manager kwarg (test_eagle_wrapper_forward):
Skip the test with a detailed TODO. The EagleWrapper interface was
refactored (resource_manager removed from __init__, sample_and_verify
removed), so the test needs a substantial rewrite. It is valuable for
validating Eagle3 acceptance ratio before the full export+transforms+
KV-cache pipeline and should be reinstated once updated.
Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
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PR_Github #39291 [ run ] triggered by Bot. Commit: |
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PR_Github #39329 [ run ] triggered by Bot. Commit: |
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Signed-off-by: Lucas Liebenwein <11156568+lucaslie@users.noreply.github.com> Signed-off-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com> Co-authored-by: Govind Ramnarayan <105831528+govind-ramnarayan@users.noreply.github.com>
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Summary by CodeRabbit
New Features
speculative_model_kwargsconfiguration field for passing additional parameters to speculative models.Improvements
Bug Fixes
Description
Test Coverage
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