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feat: move to uv first (#3545)

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* feat: move to uv first

* fix: update doc to uv first

* fix: merge dev/tests into uv pyproject

* fix: update docker docs to match current config

* fix: migrate examples to readme

* fix: add llmcompressor to conflict

* feat: rec uv sync with lockfile for dev/ci

* fix: update docker docs to clarify how to use uv images

* chore: docs

* fix: use system python, no venv

* fix: set backend cpu

* fix: only set for installing pytorch step

* fix: remove unsloth kernel and installs

* fix: remove U in tests

* fix: set backend in deps too

* chore: test

* chore: comments

* fix: attempt to lock torch

* fix: workaround torch cuda and not upgraded

* fix: forgot to push

* fix: missed source

* fix: nightly upstream loralinear config

* fix: nightly phi3 long rope not work

* fix: forgot commit

* fix: test phi3 template change

* fix: no more requirements

* fix: carry over changes from new requirements to pyproject

* chore: remove lockfile per discussion

* fix: set match-runtime

* fix: remove unneeded hf hub buildtime

* fix: duplicate cache delete on nightly

* fix: torchvision being overridden

* fix: migrate to uv images

* fix: leftover from merge

* fix: simplify base readme

* fix: update assertion message to be clearer

* chore: docs

* fix: change fallback for cicd script

* fix: match against main exactly

* fix: peft 0.19.1 change

* fix: e2e test

* fix: ci

* fix: e2e test
This commit is contained in:
NanoCode012
2026-04-21 21:16:03 +07:00
committed by GitHub
parent 323da791eb
commit 9de5b76336
58 changed files with 496 additions and 1520 deletions
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82
tests/e2e/integrations/test_scattermoe_lora_olmoe.py
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@@ -54,24 +54,8 @@ except (ImportError, ModuleNotFoundError):
)
def peft_lora_to_scattermoe(peft_A, peft_B, num_experts, rank):
peft_B_em = peft_lora_B_to_scattermoe(peft_B, num_experts, rank)
K_inter, N_hidden = peft_B.shape[0], peft_A.shape[1]
smoe_A = torch.zeros(
rank * num_experts,
K_inter,
device=peft_A.device,
dtype=peft_A.dtype,
)
smoe_B = torch.zeros(
N_hidden,
rank * num_experts,
device=peft_A.device,
dtype=peft_A.dtype,
)
for e in range(num_experts):
s = e * rank
smoe_A[s : s + rank, :] = peft_B_em[:, s : s + rank].T
smoe_B[:, s : s + rank] = peft_A[s : s + rank, :].T
smoe_A = peft_A
smoe_B = peft_lora_B_to_scattermoe(peft_B, num_experts, rank)
return smoe_A, smoe_B
def _unwrap_experts_lora(experts_module):
@@ -322,12 +306,14 @@ class TestLoRABLayoutConversion:
hidden, inter = 32, 16
scaling = 2.0
peft_A = torch.randn(E * r, hidden)
peft_B = torch.randn(inter, E * r)
# peft >=0.19.1 for down_proj [E, hidden, inter]:
# swaps in/out, lora_A [r*E, inter], lora_B [hidden, r*E]
peft_A = torch.randn(E * r, inter)
peft_B = torch.randn(hidden, E * r)
A_r = peft_A.reshape(E, r, hidden)
B_r = peft_B.reshape(inter, r, E)
delta_peft = torch.einsum("o r e, e r i -> e i o", B_r, A_r) * scaling
A_r = peft_A.reshape(E, r, inter)
B_r = peft_B.reshape(hidden, r, E)
delta_peft = torch.einsum("o r e, e r i -> e o i", B_r, A_r) * scaling
smoe_A, smoe_B = peft_lora_to_scattermoe(peft_A, peft_B, E, r)
for e in range(E):
@@ -339,31 +325,22 @@ class TestLoRABLayoutConversion:
)
def test_gate_up_proj_conversion(self):
"""Verify gate_up_proj LoRA conversion with non-square dims (Qwen3-like).
"""Verify gate_up_proj LoRA conversion with non-square dims.
gate_up_proj param: [E, 2*inter, hidden].
peft: in_features=2*inter, out_features=hidden.
peft lora_A: [r*E, 2*inter], lora_B: [hidden, r*E].
scattermoe W = param.T = [E, hidden, 2*inter], K=hidden, N=2*inter.
peft swaps in/out for 3D: lora_A [r*E, hidden], lora_B [2*inter, r*E].
scattermoe needs: lora_A [r*E, K=hidden], lora_B [N=2*inter, r*E].
Uses non-square dims (hidden=32 != 2*inter=24) to catch A<->B swap bugs.
"""
E, r = 4, 2
hidden, inter = 32, 12 # 2*inter=24 != hidden=32
scaling = 2.0
# peft assigns: in_features=2*inter, out_features=hidden
peft_A = torch.randn(E * r, 2 * inter) # [r*E, in_features=2*inter]
peft_B = torch.randn(hidden, E * r) # [out_features=hidden, r*E]
peft_A = torch.randn(E * r, hidden) # [r*E, in=hidden]
peft_B = torch.randn(2 * inter, E * r) # [out=2*inter, r*E]
# peft delta via einsum: "o r e, e r i -> e i o"
A_r = peft_A.reshape(E, r, 2 * inter)
B_r = peft_B.reshape(hidden, r, E)
delta_peft = torch.einsum("o r e, e r i -> e i o", B_r, A_r) * scaling
# delta_peft[e] has shape [in_features, out_features] = [2*inter, hidden]
# = param[e] shape [2*inter, hidden]
A_r = peft_A.reshape(E, r, hidden)
B_r = peft_B.reshape(2 * inter, r, E)
delta_peft = torch.einsum("o r e, e r i -> e o i", B_r, A_r) * scaling
smoe_A, smoe_B = peft_gate_up_lora_to_scattermoe(peft_A, peft_B, E, r)
# smoe_A should be [r*E, K=hidden], smoe_B should be [N=2*inter, r*E]
@@ -421,23 +398,21 @@ class TestPeftLoRAWeightExtraction:
r,
)
# gate_up_proj [E, 2*inter, hidden]
# peft: in_features=2*inter (dim 1), out_features=hidden (dim 2)
# gate_up_proj [E, 2*inter, hidden] — peft swaps in/out for 3D
assert trainable[
"base_model.model.moe.experts.base_layer.lora_A.default.weight"
].shape == (E * r, 2 * config.intermediate_size)
assert trainable[
"base_model.model.moe.experts.base_layer.lora_B.default.weight"
].shape == (config.hidden_size, E * r)
# down_proj [E, hidden, inter]
# peft: in_features=hidden (dim 1), out_features=inter (dim 2)
assert trainable[
"base_model.model.moe.experts.lora_A.default.weight"
].shape == (E * r, config.hidden_size)
assert trainable[
"base_model.model.moe.experts.base_layer.lora_B.default.weight"
].shape == (2 * config.intermediate_size, E * r)
# down_proj [E, hidden, inter] — peft swaps in/out for 3D
assert trainable[
"base_model.model.moe.experts.lora_A.default.weight"
].shape == (E * r, config.intermediate_size)
assert trainable[
"base_model.model.moe.experts.lora_B.default.weight"
].shape == (config.intermediate_size, E * r)
].shape == (config.hidden_size, E * r)
@requires_cuda
def test_peft_forward_runs(self):
@@ -488,8 +463,7 @@ class TestPeftLoRAWeightExtraction:
assert gup_lora is not None, "gate_up_proj LoRA not detected"
assert down_lora is not None, "down_proj LoRA not detected"
# Check shapes (after peft->scattermoe conversion with A<->B swap)
# gate_up_proj W = param.T = [E, hidden, 2*inter], K=hidden, N=2*inter
# gate_up_proj: K=hidden, N=2*inter
E, r = config.num_experts, 4
gup_A, gup_B, gup_s = gup_lora
assert gup_A.shape == (E * r, config.hidden_size), (
@@ -501,7 +475,7 @@ class TestPeftLoRAWeightExtraction:
f"{(2 * config.intermediate_size, E * r)}, got {gup_B.shape}"
)
# down_proj W = param.T = [E, inter, hidden], K=inter, N=hidden
# down_proj: K=inter, N=hidden
down_A, down_B, down_s = down_lora
assert down_A.shape == (E * r, config.intermediate_size), (
f"down_proj smoe_A: expected [r*E, K=inter]={(E * r, config.intermediate_size)}, "