diff --git a/.github/workflows/install-test.yml b/.github/workflows/install-test.yml index 99c4e6cec5..b27a53de40 100644 --- a/.github/workflows/install-test.yml +++ b/.github/workflows/install-test.yml @@ -5,14 +5,18 @@ on: branches: [main] paths: - 'pyproject.toml' + - 'pyproject.vllm.toml' - 'uv.lock' + - 'uv.vllm.lock' - 'areal/**' - '.github/workflows/install-test.yml' push: branches: [main] paths: - 'pyproject.toml' + - 'pyproject.vllm.toml' - 'uv.lock' + - 'uv.vllm.lock' - 'areal/**' - '.github/workflows/install-test.yml' workflow_dispatch: @@ -39,7 +43,9 @@ jobs: uses: astral-sh/setup-uv@v7 with: enable-cache: true - cache-dependency-glob: 'uv.lock' + cache-dependency-glob: | + uv.lock + uv.vllm.lock - name: Set up Python run: uv python install ${{ matrix.python-version }} @@ -93,7 +99,9 @@ jobs: uses: astral-sh/setup-uv@v7 with: enable-cache: true - cache-dependency-glob: 'uv.lock' + cache-dependency-glob: | + uv.lock + uv.vllm.lock - name: Set up Python run: uv python install 3.12 @@ -101,7 +109,12 @@ jobs: - name: Install package with CUDA extras (excluding flash-attn) # flash-attn requires CUDA toolkit for compilation, skip it in CI # Test individual extras that have pre-built wheels - run: uv sync --extra ${{ matrix.variant }} --extra megatron --extra tms + run: | + if [ "${{ matrix.variant }}" = "vllm" ]; then + cp pyproject.vllm.toml pyproject.toml + cp uv.vllm.lock uv.lock + fi + uv sync --extra ${{ matrix.variant }} --extra megatron --extra tms - name: Verify package import with CUDA extras run: | diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 29ec58f2e0..e5959f0da0 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -64,6 +64,18 @@ repos: name: nbstripout - Strip notebook output description: Strip output from Jupyter notebooks + # Check consistency between pyproject.toml variants (sglang vs vllm) + - repo: local + hooks: + - id: check-pyproject-consistency + name: Check pyproject.toml consistency + entry: python3 areal/tools/check_pyproject_consistency.py + language: system + files: ^pyproject(\.vllm)?\.toml$ + pass_filenames: false + always_run: false + require_serial: true + # Generate CLI documentation - repo: local hooks: diff --git a/AGENTS.md b/AGENTS.md index a0ac4854c2..c47b11681f 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -8,7 +8,7 @@ ```bash # Environment -uv sync --extra cuda # CUDA + SGLang inference (default); for vLLM: --extra cuda-vllm +uv sync --extra cuda # CUDA + SGLang inference (default); for vLLM: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda source .venv/bin/activate # activate venv BEFORE pre-commit or git commit if venv exists pre-commit install --install-hooks # hooks: Ruff, clang-format, mdformat, nbstripout, conventional-commits pre-commit run --all-files # lint + format everything diff --git a/CLAUDE.md b/CLAUDE.md index bdc9dc5db4..acb7da077d 100644 --- a/CLAUDE.md +++ b/CLAUDE.md @@ -40,6 +40,7 @@ uv --version # Install: https://docs.astral.sh/uv/ # Sync dependencies uv sync --extra cuda # CUDA + SGLang inference (default) +# For vLLM: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda uv sync --group dev # Include dev/test packages uv run python3 areal/tools/validate_installation.py # Validate installation diff --git a/Dockerfile b/Dockerfile index cd232b8f55..d77711b83a 100644 --- a/Dockerfile +++ b/Dockerfile @@ -218,10 +218,16 @@ RUN uv pip install nanobot-ai # Install the project's dependencies (not the project itself) # This adds packages without removing unlisted ones (like our C++ packages) -# VARIANT selects the inference backend (sglang or vllm) +# VARIANT selects the inference backend (sglang or vllm) via separate pyproject files RUN --mount=type=cache,target=/root/.cache/uv \ --mount=type=bind,source=pyproject.toml,target=pyproject.toml \ - uv pip install --no-build-isolation -r pyproject.toml --extra cuda-train --extra ${VARIANT} --group dev + --mount=type=bind,source=pyproject.vllm.toml,target=pyproject.vllm.toml \ + case "$VARIANT" in \ + sglang) cp pyproject.toml /tmp/pyproject.toml ;; \ + vllm) cp pyproject.vllm.toml /tmp/pyproject.toml ;; \ + *) echo "Invalid VARIANT=$VARIANT (expected: sglang|vllm)" >&2; exit 1 ;; \ + esac \ + && uv pip install --no-build-isolation -r /tmp/pyproject.toml --extra cuda --group dev ############################################################## # STAGE 4: Misc fixes and final setup diff --git a/README.md b/README.md index 2956718233..0db0b70842 100644 --- a/README.md +++ b/README.md @@ -106,6 +106,7 @@ pip install uv # (pick the wheel matching your Python version; see https://github.com/mjun0812/flash-attention-prebuild-wheels/releases) uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.9-cp312-cp312-linux_x86_64.whl" uv sync --extra cuda # installs training packages + SGLang (default inference backend) +# For vLLM instead: cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda ``` Our training scripts automatically download the required dataset (openai/gsm8k) and @@ -277,8 +278,8 @@ pip install uv uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.9-cp312-cp312-linux_x86_64.whl" # Use `--extra cuda` on Linux with CUDA (installs training packages + SGLang) uv sync --extra cuda --group dev -# For vLLM instead (note: use torch2.10 flash-attn wheel): -# uv sync --extra cuda-vllm --group dev +# For vLLM instead: +# cp pyproject.vllm.toml pyproject.toml && cp uv.vllm.lock uv.lock && uv sync --extra cuda --group dev # Or without CUDA support # uv sync --group dev diff --git a/areal/engine/core/train_engine.py b/areal/engine/core/train_engine.py index ef1bb3b917..eecd1e9ee6 100644 --- a/areal/engine/core/train_engine.py +++ b/areal/engine/core/train_engine.py @@ -56,8 +56,10 @@ def compute_total_loss_weight( .clone() .to(dtype=torch.float32) ) - assert total_weight != 0, "Total loss weight must be non-zero" dist.all_reduce(total_weight, group=dp_group) + assert total_weight > 0, ( + "Global total loss weight must be positive after all_reduce" + ) return total_weight diff --git a/areal/engine/fsdp_engine.py b/areal/engine/fsdp_engine.py index 166e49893f..15c7e66308 100644 --- a/areal/engine/fsdp_engine.py +++ b/areal/engine/fsdp_engine.py @@ -1658,6 +1658,10 @@ def _compute_logprobs_and_loss( loss_multiplier: float = 1.0, ) -> torch.Tensor: """Compute logprobs/entropy and return scaled loss.""" + local_weight = loss_weight_fn(ctx.mb_input) + if local_weight == 0: + return logits.mean() * 0.0 + if self.config.is_critic and self.enable_tree_training: raise NotImplementedError( "Tree training with critic model is not supported yet." @@ -1669,7 +1673,7 @@ def _compute_logprobs_and_loss( if ctx.trie_node is None or not ctx.trie_node.all_sequence_ids: # Return zero loss that maintains gradient connection to logits # This ensures backward() works correctly for FSDP synchronization - return logits.sum() * 0.0 + return logits.mean() * 0.0 # For tree training, use gather_packed_tree_vocab_stats to properly # unpack vocab stats from tree structure back to per-sequence format. @@ -1712,7 +1716,7 @@ def _compute_logprobs_and_loss( values = values[: -ctx.pad_length] loss = loss_fn(values, ctx.mb_input) - loss_scale = loss_weight_fn(ctx.mb_input) / total_loss_weight * loss_multiplier + loss_scale = local_weight / total_loss_weight * loss_multiplier return loss * loss_scale def _compute_forward_result( diff --git a/areal/engine/fsdp_utils/grad.py b/areal/engine/fsdp_utils/grad.py index 48fd5c6e7c..290db79812 100644 --- a/areal/engine/fsdp_utils/grad.py +++ b/areal/engine/fsdp_utils/grad.py @@ -99,11 +99,22 @@ def get_grad_norm_fp32( norm_type = float(norm_type) total_norm = 0.0 - if not grads_for_norm: - return 0.0 - device = current_platform.current_device() + if not grads_for_norm: + # Still participate in all_reduce with zero contribution so that + # ranks with grads don't hang waiting for this rank (e.g. LoRA frozen ranks). + total_norm_cuda = torch.tensor(0.0, dtype=torch.float, device=device) + reduce_op = dist.ReduceOp.MAX if norm_type == torch.inf else dist.ReduceOp.SUM + if data_parallel_group: + dist.all_reduce(total_norm_cuda, op=reduce_op, group=data_parallel_group) + if model_parallel_group is not None: + dist.all_reduce(total_norm_cuda, op=reduce_op, group=model_parallel_group) + total_norm = float(total_norm_cuda.item()) + if norm_type != torch.inf and total_norm > 0: + total_norm = total_norm ** (1.0 / norm_type) + return total_norm + if norm_type == torch.inf: norms = [grad.abs().max() for grad in grads_for_norm] total_norm = torch.max(torch.stack(norms)) if norms else 0.0 diff --git a/areal/engine/megatron_engine.py b/areal/engine/megatron_engine.py index aabe64b44d..eb486d8530 100644 --- a/areal/engine/megatron_engine.py +++ b/areal/engine/megatron_engine.py @@ -1646,6 +1646,10 @@ def _compute_logprobs_and_loss( total_loss_weight: torch.Tensor, loss_multiplier: float = 1.0, ) -> torch.Tensor: + local_weight = loss_weight_fn(inputs) + if local_weight == 0: + return output.mean() * 0.0 + if self.config.is_critic and self.enable_tree_training: raise NotImplementedError( "Tree training with critic model is not supported yet." @@ -1658,7 +1662,7 @@ def _compute_logprobs_and_loss( if trie_node is None or not trie_node.all_sequence_ids: # Return zero loss that maintains gradient connection to output # This ensures backward() works correctly for distributed synchronization - return output.sum() * 0.0 + return output.mean() * 0.0 # For tree training, use gather_packed_tree_vocab_stats to properly # unpack vocab stats from tree structure back to per-sequence format. @@ -1699,7 +1703,7 @@ def _compute_logprobs_and_loss( values = output.squeeze(-1) loss = loss_fn(values, inputs) - loss_scale = loss_weight_fn(inputs) / total_loss_weight * loss_multiplier + loss_scale = local_weight / total_loss_weight * loss_multiplier return loss * loss_scale def _compute_forward_result( diff --git a/areal/engine/vllm_ext/areal_vllm_server.py b/areal/engine/vllm_ext/areal_vllm_server.py index 50dd06dff7..c3a37316a9 100644 --- a/areal/engine/vllm_ext/areal_vllm_server.py +++ b/areal/engine/vllm_ext/areal_vllm_server.py @@ -164,6 +164,7 @@ async def areal_update_weight(request: UpdateWeightsRequest, raw_request: Reques logger.info(f"API server starts areal_update_weight, {request.model_path}") llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc( "areal_update_weights", @@ -183,6 +184,7 @@ async def areal_update_weight_lora( ) llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc( @@ -205,6 +207,7 @@ async def areal_update_weight_xccl(raw_request: Request): logger.info("API server starts areal_update_weight_xccl") llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc("areal_update_weight_xccl") finally: @@ -219,6 +222,7 @@ async def areal_update_weight_lora_xccl( logger.info("API server starts areal_update_weight_lora_xccl") llm = raw_request.app.state.engine_client await llm.pause_generation(wait_for_inflight_requests=False, clear_cache=True) + await llm.reset_mm_cache() try: ret_list = await llm.collective_rpc("areal_update_weight_lora_xccl") @@ -310,6 +314,7 @@ async def areal_pause_generation(raw_request: Request): wait_for_inflight_requests=False, clear_cache=True, ) + await llm.reset_mm_cache() return to_json_response(True, "Generation paused and all requests aborted") diff --git a/areal/engine/vllm_ext/vllm_worker_extension.py b/areal/engine/vllm_ext/vllm_worker_extension.py index 126f884e27..4c91a3a982 100644 --- a/areal/engine/vllm_ext/vllm_worker_extension.py +++ b/areal/engine/vllm_ext/vllm_worker_extension.py @@ -180,7 +180,7 @@ def areal_update_weight_lora_xccl(self): f"LoRA adapter {lora_int_id} not found. Available: {adapter_ids}" ) - # Get the LoRA model + # Get the currently registered LoRA model (used for diagnostics). lora_model = ( self.model_runner.lora_manager._adapter_manager._registered_adapters[ lora_int_id @@ -211,12 +211,33 @@ def areal_update_weight_lora_xccl(self): logger.info(f"Received {len(received_weights)} LoRA parameters via XCCL") - self.model_runner.lora_manager.remove_adapter(lora_int_id) - normalized_weights = { k.replace("default.", ""): v for k, v in received_weights.items() } + lora_partial_shard_key = (self.areal_lora_name, lora_int_id) + + group_shards = self._lora_partial_shards.setdefault( + lora_partial_shard_key, {} + ) + group_shards[self.areal_weight_meta_group_name] = normalized_weights + buffered_count = len(group_shards) + + # Assumes that every registered weight update group contributes to the update cycle + if buffered_count < len(self.weight_update_groups): + logger.info( + "Buffered LoRA shard for " + f"{self.areal_lora_name}: group={self.areal_weight_meta_group_name}, " + f"buffered={buffered_count}/{len(self.weight_update_groups)} PP stages." + ) + self.sync() + return True, "Success" + + merged_weights: dict[str, torch.Tensor] = {} + for shard in group_shards.values(): + merged_weights.update(shard) + self._lora_partial_shards.pop(lora_partial_shard_key, None) + peft_config = { "r": self.areal_lora_rank, "lora_alpha": self.areal_lora_alpha, @@ -234,7 +255,7 @@ def areal_update_weight_lora_xccl(self): new_lora_model = LoRAModel.from_lora_tensors( lora_model_id=self.areal_lora_int_id, - tensors=normalized_weights, + tensors=merged_weights, peft_helper=peft_helper, device=self.model_runner.device, dtype=self.model_runner.lora_manager.lora_config.lora_dtype, @@ -244,13 +265,21 @@ def areal_update_weight_lora_xccl(self): ), ) + self.model_runner.lora_manager.remove_adapter(lora_int_id) + self.model_runner.lora_manager._adapter_manager._add_adapter(new_lora_model) self.model_runner.lora_manager._adapter_manager.activate_adapter( new_lora_model.id ) logger.info( - f"Found LoRA model with {len(new_lora_model.loras)} LoRA modules" + f"Updated New LoRA model with {len(new_lora_model.loras)} LoRA modules " + f"from {len(merged_weights)} tensors across {len(self.weight_update_groups)} groups" ) + if len(new_lora_model.loras) != len(lora_model.loras): + logger.warning( + f"Number of modules in the new LoRA model ({len(new_lora_model.loras)}) " + f"does not match the old LoRA model ({len(lora_model.loras)})." + ) self.sync() return True, "Success" @@ -271,6 +300,18 @@ def areal_init_update_weight_group( ): if not hasattr(self, "weight_update_groups"): self.weight_update_groups: dict[str, dist.ProcessGroup] = {} + + # This is required for buffering weights during lora weight update, as vLLM + # expects the partial PP shards to be buffered until all groups have sent their shards. + _is_vllm_lora_enabled = ( + getattr(self.model_runner, "lora_manager", None) is not None + ) + if _is_vllm_lora_enabled and not hasattr(self, "_lora_partial_shards"): + # (lora_name, lora_int_id) -> group_name -> normalized weight dict + self._lora_partial_shards: dict[ + tuple[str, int], dict[str, dict[str, torch.Tensor]] + ] = {} + try: group = init_custom_process_group( backend=backend, diff --git a/areal/experimental/engine/archon_engine.py b/areal/experimental/engine/archon_engine.py index 3b0242ac4a..5a02eca139 100644 --- a/areal/experimental/engine/archon_engine.py +++ b/areal/experimental/engine/archon_engine.py @@ -1154,10 +1154,14 @@ def _compute_logprobs_and_loss( loss_multiplier: float = 1.0, ) -> torch.Tensor: """Compute logprobs/entropy and return scaled loss.""" + local_weight = loss_weight_fn(ctx.mb_input) + if local_weight == 0: + return logits.mean() * 0.0 + if not self.config.is_critic: result = self._gather_actor_train_outputs(logits, ctx) if result is None: - return logits.sum() * 0.0 + return logits.mean() * 0.0 logprobs, entropy, vocab_min_logits, vocab_max_logits = result loss = loss_fn( logprobs, @@ -1170,7 +1174,7 @@ def _compute_logprobs_and_loss( values = self._gather_critic_output(logits, ctx) loss = loss_fn(values, ctx.mb_input) - loss_scale = loss_weight_fn(ctx.mb_input) / total_loss_weight * loss_multiplier + loss_scale = local_weight / total_loss_weight * loss_multiplier return loss * loss_scale def _compute_forward_result( diff --git a/areal/experimental/engine/archon_runner.py b/areal/experimental/engine/archon_runner.py index f50ce0a264..517d6a61f5 100644 --- a/areal/experimental/engine/archon_runner.py +++ b/areal/experimental/engine/archon_runner.py @@ -273,13 +273,13 @@ def pp_loss_fn(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: pred = pred.squeeze(0) loss = process_output_fn(pred, ctx.to_dict()) if loss is None: - return pred.sum() * 0.0 + return pred.mean() * 0.0 return loss else: # Non-last stage: dummy loss that keeps all elements in computation graph # so autograd can compute complete pred.grad for upstream stage def pp_loss_fn(pred: torch.Tensor, target: torch.Tensor) -> torch.Tensor: - return pred.sum() * 0.0 + return pred.mean() * 0.0 return pp_loss_fn diff --git a/areal/infra/controller/train_controller.py b/areal/infra/controller/train_controller.py index d351664662..59bef0a85b 100644 --- a/areal/infra/controller/train_controller.py +++ b/areal/infra/controller/train_controller.py @@ -21,6 +21,7 @@ from areal.infra.rpc.rtensor import RTensor from areal.infra.utils.concurrent import run_async_task from areal.utils import logging, stats_tracker +from areal.utils.data import make_dummy_eval_item from areal.utils.network import find_free_ports from areal.utils.seqpack import balanced_greedy_partition @@ -55,34 +56,99 @@ def _is_tensor_like(obj: Any) -> bool: ) +def _item_weight(d: dict[str, Any]) -> int: + attn_mask = d.get("attention_mask") + if isinstance(attn_mask, torch.Tensor): + return int(attn_mask.sum().item()) + if isinstance(attn_mask, RTensor): + return attn_mask.data.numel() + # Fallback: first tensor's numel + for v in d.values(): + if isinstance(v, RTensor): + return v.data.numel() + if isinstance(v, torch.Tensor) and v.ndim >= 2: + return v.numel() + return 1 + + def _dispatch_tensors( item_list: list[dict[str, Any]], dp_size: int, + group_size: int = 1, ) -> tuple[list[list[dict[str, Any]]], list[list[int]]]: - """Partition trajectories across DP groups by balanced token count.""" - token_weights: list[int] = [] - for d in item_list: - attn_mask = d.get("attention_mask") - if isinstance(attn_mask, torch.Tensor): - token_weights.append(int(attn_mask.sum().item())) - elif isinstance(attn_mask, RTensor): - token_weights.append(attn_mask.data.numel()) - else: - # Fallback: first tensor's numel - w = 1 - for v in d.values(): - if isinstance(v, RTensor): - w = v.data.numel() - break - if isinstance(v, torch.Tensor) and v.ndim >= 2: - w = v.numel() - break - token_weights.append(w) - group_indices = balanced_greedy_partition(token_weights, K=dp_size) - splits = [[item_list[i] for i in idxs] for idxs in group_indices] + """Partition trajectories across DP groups by balanced token count. + + Args: + group_size: number of consecutive items that form an atomic dispatch + unit (e.g. 2 for chosen/rejected RW pairs). Groups are never + split across DP ranks. ``group_size=1`` degenerates to per-item + partitioning. + """ + n = len(item_list) + if n % group_size != 0: + raise ValueError( + f"item count ({n}) must be divisible by group_size ({group_size})" + ) + + token_weights = [_item_weight(d) for d in item_list] + n_groups = n // group_size + + group_weights = [ + sum(token_weights[g * group_size + k] for k in range(group_size)) + for g in range(n_groups) + ] + + gpart = balanced_greedy_partition(group_weights, K=dp_size) + + group_indices: list[list[int]] = [] + splits: list[list[dict[str, Any]]] = [] + for gidxs in gpart: + item_idxs: list[int] = [] + items: list[dict[str, Any]] = [] + for g in gidxs: + for k in range(group_size): + idx = g * group_size + k + item_idxs.append(idx) + items.append(item_list[idx]) + group_indices.append(item_idxs) + splits.append(items) + + assert all(len(s) % group_size == 0 for s in splits), ( + f"Post-dispatch invariant violated: shard sizes " + f"{[len(s) for s in splits]} not all divisible by group_size={group_size}" + ) return splits, group_indices +def _pad_eval_batch( + args: tuple[Any, ...], dp_size: int, group_size: int = 1 +) -> tuple[Any, ...]: + """Pad the first tensor-like arg to a multiple of ``dp_size * group_size``. + + Called before dispatch for explicit evaluation controller paths so that + ``balanced_greedy_partition`` always receives a divisible input. + Dummy items have zero attention/loss masks and contribute nothing + to metrics or loss. + """ + result = list(args) + pad_target = dp_size * group_size + for i, arg in enumerate(result): + if isinstance(arg, list) and arg and _is_tensor_like(arg): + n = len(arg) + pad_count = (-n) % pad_target + if pad_count > 0: + padded = list(arg) + template = arg[0] + padded.extend(make_dummy_eval_item(template) for _ in range(pad_count)) + result[i] = padded + logger.info( + f"Eval dispatch: padded {pad_count} dummy items " + f"(total {len(padded)}) for dp_size={dp_size}" + ) + break # only pad the first tensor-like arg + return tuple(result) + + def _merge_tensors( results: list[Any], group_indices: list[list[int]] ) -> list[Any] | None: @@ -388,6 +454,20 @@ async def _async_custom_function_call(self, method: str, *args, **kwargs): results = await self._call_workers(method, dp_args, dp_kwargs) return self._collect_results(results, group_indices) + def _pad_eval_dispatch_args( + self, + args: tuple[Any, ...], + kwargs: dict[str, Any], + *, + group_size: int, + ) -> tuple[tuple[Any, ...], dict[str, Any]]: + """Pad eval batches for explicit algorithm-level evaluation dispatch.""" + kwargs = dict(kwargs) + args = _pad_eval_batch( + args, self.parallel_strategy.dp_size, group_size=group_size + ) + return args, kwargs + def _prepare_dispatch( self, *args, **kwargs ) -> tuple[list[list[Any]], dict[str, list[Any]], list[list[int]] | None]: @@ -396,12 +476,13 @@ def _prepare_dispatch( Returns (dp_split_args, dp_split_kwargs, group_indices). group_indices is non-None only for tensor dispatches. """ + group_size = kwargs.pop("group_size", 1) if _is_tensor_like(args) or _is_tensor_like(kwargs): - return self._partition_inputs(*args, **kwargs) + return self._partition_inputs(group_size, *args, **kwargs) return self._replicate_inputs(*args, **kwargs) def _partition_inputs( - self, *args, **kwargs + self, group_size: int, /, *args, **kwargs ) -> tuple[list[list[Any]], dict[str, list[Any]], list[list[int]]]: """Partition tensor args across DP groups; replicate others.""" dp_size = self.parallel_strategy.dp_size @@ -411,7 +492,9 @@ def _split(item: Any) -> list[Any]: nonlocal group_indices if _is_tensor_like(item): if group_indices is None: - splits, group_indices = _dispatch_tensors(item, dp_size) + splits, group_indices = _dispatch_tensors( + item, dp_size, group_size=group_size + ) return splits return [[item[i] for i in idxs] for idxs in group_indices] return [item] * dp_size diff --git a/areal/tools/check_pyproject_consistency.py b/areal/tools/check_pyproject_consistency.py new file mode 100644 index 0000000000..7c12353080 --- /dev/null +++ b/areal/tools/check_pyproject_consistency.py @@ -0,0 +1,412 @@ +#!/usr/bin/env python3 +"""Check consistency between pyproject.toml and pyproject.vllm.toml. + +Only packages marked as "escapable" (inference-backend-specific) are allowed +to have different versions or be present in only one file. Everything else +must be identical across the two project configurations. + +Usage:: + + python areal/tools/check_pyproject_consistency.py + python areal/tools/check_pyproject_consistency.py pyproject.toml pyproject.vllm.toml + +Exit codes: + 0 — files are consistent + 1 — inconsistencies found (details printed to stderr) +""" + +from __future__ import annotations + +import re +import sys +import tomllib +from pathlib import Path +from typing import Any + +# ── Escapable packages ────────────────────────────────────────────────────── +# These packages are expected to differ between the SGLang (default) and vLLM +# variants because the two inference backends pin mutually-incompatible +# versions of torch / torchao / etc. +ESCAPABLE_PACKAGES: frozenset[str] = frozenset( + { + "torch", + "torchao", + "torchaudio", + "torchvision", + "sglang", + "vllm", + "nvidia-cudnn-cu12", + "openai", + "soundfile", + } +) + +# Optional-dependency extras that are backend-specific. +# These extras are expected to exist exclusively in one variant. +BACKEND_EXTRAS: frozenset[str] = frozenset({"sglang", "vllm"}) + +# ── Helpers ───────────────────────────────────────────────────────────────── + + +def _normalize_name(name: str) -> str: + """PEP 503-normalize a package name (lowercase, ``[-_.]+`` → ``-``).""" + return re.sub(r"[-_.]+", "-", name.strip()).lower() + + +def _parse_dep_name(dep: str) -> str: + """Extract the normalized package name from a dependency string. + + Examples:: + + "torch==2.9.1+cu129; ..." → "torch" + "sglang[tracing]==0.5.9" → "sglang" + "areal[cuda-train]" → "areal" + """ + match = re.match(r"([A-Za-z0-9][-A-Za-z0-9_.]*)", dep.strip()) + if not match: + return dep.strip() + return _normalize_name(match.group(1)) + + +def _dep_list_to_dict(deps: list[str]) -> dict[str, list[str]]: + """Map normalized package name → list of full dependency strings. + + A package may have multiple entries (e.g. platform-specific markers). + """ + result: dict[str, list[str]] = {} + for dep in deps: + name = _parse_dep_name(dep) + result.setdefault(name, []).append(dep.strip()) + return result + + +def _is_escapable(name: str) -> bool: + return _normalize_name(name) in {_normalize_name(n) for n in ESCAPABLE_PACKAGES} + + +def _is_backend_selfref(dep: str) -> bool: + """True if *dep* is a self-reference to a backend-specific extra. + + E.g. ``areal[sglang]`` or ``areal[vllm]``. + """ + match = re.match(r"\s*areal\[([^\]]+)\]", dep.strip()) + return match is not None and match.group(1) in BACKEND_EXTRAS + + +# ── Comparison engine ─────────────────────────────────────────────────────── + + +class _Checker: + """Accumulates errors while comparing two parsed TOML dicts.""" + + def __init__(self, file_a: str, file_b: str) -> None: + self.file_a = file_a + self.file_b = file_b + self.errors: list[str] = [] + + def _err(self, msg: str) -> None: + self.errors.append(msg) + + # ── generic deep comparison ───────────────────────────────────────── + + def _cmp_values(self, path: str, a: Any, b: Any) -> None: + if type(a) is not type(b): + self._err( + f"{path}: type mismatch — {self.file_a} has " + f"{type(a).__name__}, {self.file_b} has {type(b).__name__}" + ) + return + if isinstance(a, dict): + self._cmp_dicts(path, a, b) + elif isinstance(a, list): + if a != b: + self._err( + f"{path}: lists differ\n {self.file_a}: {a}\n {self.file_b}: {b}" + ) + elif a != b: + self._err(f"{path}: {self.file_a}={a!r} ≠ {self.file_b}={b!r}") + + def _cmp_dicts(self, path: str, a: dict, b: dict) -> None: + for key in sorted(set(a) | set(b)): + sub = f"{path}.{key}" if path else key + if key not in a: + self._err(f"{sub}: missing in {self.file_a}") + elif key not in b: + self._err(f"{sub}: missing in {self.file_b}") + else: + self._cmp_values(sub, a[key], b[key]) + + # ── [project].dependencies ────────────────────────────────────────── + + def check_dependencies(self, deps_a: list[str], deps_b: list[str]) -> None: + dict_a = _dep_list_to_dict(deps_a) + dict_b = _dep_list_to_dict(deps_b) + for name in sorted(set(dict_a) | set(dict_b)): + if _is_escapable(name): + continue + in_a, in_b = name in dict_a, name in dict_b + if in_a and not in_b: + self._err( + f"dependencies: non-escapable package {name!r} " + f"in {self.file_a} but missing in {self.file_b}" + ) + elif in_b and not in_a: + self._err( + f"dependencies: non-escapable package {name!r} " + f"in {self.file_b} but missing in {self.file_a}" + ) + elif sorted(dict_a[name]) != sorted(dict_b[name]): + self._err( + f"dependencies: non-escapable package {name!r} differs\n" + f" {self.file_a}: {dict_a[name]}\n" + f" {self.file_b}: {dict_b[name]}" + ) + + # ── [project.optional-dependencies] ───────────────────────────────── + + def check_optional_deps( + self, + extras_a: dict[str, list[str]], + extras_b: dict[str, list[str]], + ) -> None: + all_extras = sorted(set(extras_a) | set(extras_b)) + for extra in all_extras: + # Backend-specific extras are expected to be exclusive. + if extra in BACKEND_EXTRAS: + continue + + if extra not in extras_a: + self._err( + f"optional-dependencies: extra {extra!r} missing in {self.file_a}" + ) + continue + if extra not in extras_b: + self._err( + f"optional-dependencies: extra {extra!r} missing in {self.file_b}" + ) + continue + + # Filter out backend-specific self-references before comparing. + filtered_a = sorted( + d for d in extras_a[extra] if not _is_backend_selfref(d) + ) + filtered_b = sorted( + d for d in extras_b[extra] if not _is_backend_selfref(d) + ) + if filtered_a != filtered_b: + self._err( + f"optional-dependencies.{extra}: differs " + f"(after filtering backend self-refs)\n" + f" {self.file_a}: {filtered_a}\n" + f" {self.file_b}: {filtered_b}" + ) + + # ── [tool.uv].override-dependencies ───────────────────────────────── + + def check_override_deps( + self, overrides_a: list[str], overrides_b: list[str] + ) -> None: + dict_a = _dep_list_to_dict(overrides_a) + dict_b = _dep_list_to_dict(overrides_b) + for name in sorted(set(dict_a) | set(dict_b)): + if _is_escapable(name): + continue + in_a, in_b = name in dict_a, name in dict_b + if in_a and not in_b: + self._err( + f"tool.uv.override-dependencies: {name!r} " + f"in {self.file_a} but missing in {self.file_b}" + ) + elif in_b and not in_a: + self._err( + f"tool.uv.override-dependencies: {name!r} " + f"in {self.file_b} but missing in {self.file_a}" + ) + elif sorted(dict_a[name]) != sorted(dict_b[name]): + self._err( + f"tool.uv.override-dependencies: " + f"non-escapable {name!r} differs\n" + f" {self.file_a}: {dict_a[name]}\n" + f" {self.file_b}: {dict_b[name]}" + ) + + # ── [tool.uv.sources] ────────────────────────────────────────────── + + def check_uv_sources( + self, sources_a: dict[str, Any], sources_b: dict[str, Any] + ) -> None: + for pkg in sorted(set(sources_a) | set(sources_b)): + if pkg not in sources_a: + self._err(f"tool.uv.sources: {pkg!r} missing in {self.file_a}") + elif pkg not in sources_b: + self._err(f"tool.uv.sources: {pkg!r} missing in {self.file_b}") + elif _is_escapable(pkg): + # Just verify index names match (extra names may differ). + idx_a = sorted( + {e.get("index", "") for e in sources_a[pkg] if isinstance(e, dict)} + ) + idx_b = sorted( + {e.get("index", "") for e in sources_b[pkg] if isinstance(e, dict)} + ) + if idx_a != idx_b: + self._err( + f"tool.uv.sources.{pkg}: index names differ — " + f"{self.file_a}={idx_a}, {self.file_b}={idx_b}" + ) + elif sources_a[pkg] != sources_b[pkg]: + self._err( + f"tool.uv.sources.{pkg}: differs (non-escapable)\n" + f" {self.file_a}: {sources_a[pkg]}\n" + f" {self.file_b}: {sources_b[pkg]}" + ) + + # ── [tool.uv] (top-level) ────────────────────────────────────────── + + def check_tool_uv(self, uv_a: dict, uv_b: dict) -> None: + special_keys = {"override-dependencies", "sources", "conflicts"} + + self.check_override_deps( + uv_a.get("override-dependencies", []), + uv_b.get("override-dependencies", []), + ) + self.check_uv_sources( + uv_a.get("sources", {}), + uv_b.get("sources", {}), + ) + # `conflicts` may be structurally different (single-backend needs + # none); skip comparison. + + for key in sorted(set(uv_a) | set(uv_b)): + if key in special_keys: + continue + sub = f"tool.uv.{key}" + if key not in uv_a: + self._err(f"{sub}: missing in {self.file_a}") + elif key not in uv_b: + self._err(f"{sub}: missing in {self.file_b}") + else: + self._cmp_values(sub, uv_a[key], uv_b[key]) + + # ── entry point ───────────────────────────────────────────────────── + + def run(self, toml_a: dict, toml_b: dict) -> int: + # 1. build-system + self._cmp_values( + "build-system", + toml_a.get("build-system", {}), + toml_b.get("build-system", {}), + ) + + # 2. project metadata (everything except deps) + proj_a = toml_a.get("project", {}) + proj_b = toml_b.get("project", {}) + skip = {"dependencies", "optional-dependencies"} + meta_a = {k: v for k, v in proj_a.items() if k not in skip} + meta_b = {k: v for k, v in proj_b.items() if k not in skip} + if meta_a != meta_b: + self._cmp_dicts("project", meta_a, meta_b) + + # 3. dependencies + self.check_dependencies( + proj_a.get("dependencies", []), + proj_b.get("dependencies", []), + ) + + # 4. optional-dependencies + self.check_optional_deps( + proj_a.get("optional-dependencies", {}), + proj_b.get("optional-dependencies", {}), + ) + + # 5. dependency-groups + self._cmp_values( + "dependency-groups", + toml_a.get("dependency-groups", {}), + toml_b.get("dependency-groups", {}), + ) + + # 6. tool.uv + self.check_tool_uv( + toml_a.get("tool", {}).get("uv", {}), + toml_b.get("tool", {}).get("uv", {}), + ) + + # 7. Other tool sections (pytest, ruff, etc.) + tool_a = toml_a.get("tool", {}) + tool_b = toml_b.get("tool", {}) + for key in sorted(set(tool_a) | set(tool_b)): + if key == "uv": + continue + sub = f"tool.{key}" + if key not in tool_a: + self._err(f"{sub}: missing in {self.file_a}") + elif key not in tool_b: + self._err(f"{sub}: missing in {self.file_b}") + else: + self._cmp_values(sub, tool_a[key], tool_b[key]) + + if self.errors: + print( + f"❌ {len(self.errors)} inconsistenc" + f"{'y' if len(self.errors) == 1 else 'ies'} " + f"between {self.file_a} and {self.file_b}:", + file=sys.stderr, + ) + for e in self.errors: + print(f" {e}", file=sys.stderr) + return 1 + + print( + f"✅ {self.file_a} and {self.file_b} are consistent " + f"(escapable: {', '.join(sorted(ESCAPABLE_PACKAGES))})" + ) + return 0 + + +# ── CLI ───────────────────────────────────────────────────────────────────── + + +def main(argv: list[str] | None = None) -> int: + import argparse + + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "files", + nargs="*", + default=["pyproject.toml", "pyproject.vllm.toml"], + help=( + "Pair of pyproject files to compare " + "(default: pyproject.toml pyproject.vllm.toml)" + ), + ) + args = parser.parse_args(argv) + + if len(args.files) != 2: + print( + f"Error: exactly two pyproject files required, got {len(args.files)}", + file=sys.stderr, + ) + return 1 + + file_a, file_b = args.files + path_a, path_b = Path(file_a), Path(file_b) + + for p in (path_a, path_b): + if not p.exists(): + print(f"Error: {p} not found", file=sys.stderr) + return 1 + + with open(path_a, "rb") as f: + toml_a = tomllib.load(f) + with open(path_b, "rb") as f: + toml_b = tomllib.load(f) + + checker = _Checker(file_a, file_b) + return checker.run(toml_a, toml_b) + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/areal/trainer/rw/rw_engine.py b/areal/trainer/rw/rw_engine.py index 781f5a344f..5331732082 100644 --- a/areal/trainer/rw/rw_engine.py +++ b/areal/trainer/rw/rw_engine.py @@ -4,7 +4,6 @@ from areal.api import TrainEngine from areal.infra import TrainController -from areal.infra.platforms import current_platform from areal.utils import logging, stats_tracker from areal.utils.data import batched_call from areal.utils.perf_tracer import trace_perf @@ -12,6 +11,27 @@ logger = logging.getLogger("RWEngine") +def _rw_valid_pairs(x: dict[str, Any]) -> torch.Tensor: + seqlens = x["cu_seqlens"][1:] - x["cu_seqlens"][:-1] + return seqlens.view(-1, 2).ne(0).all(dim=1) + + +def _rw_loss_weight(x: dict[str, Any]) -> torch.Tensor: + return _rw_valid_pairs(x).count_nonzero().float() + + +def _log_empty_rw_stats(device: torch.device) -> None: + n_pairs = torch.zeros(1, dtype=torch.bool, device=device) + stats_tracker.denominator(n_pairs=n_pairs) + stats_tracker.stat( + correct_ratio=torch.zeros(1, dtype=torch.float32, device=device), + pos_score=torch.zeros(1, dtype=torch.float32, device=device), + neg_score=torch.zeros(1, dtype=torch.float32, device=device), + loss=torch.zeros(1, dtype=torch.float32, device=device), + denominator="n_pairs", + ) + + class RWEngine: def __init__(self, engine: TrainEngine): self.engine = engine @@ -23,15 +43,13 @@ def train_rw(self, data: list[dict[str, Any]]) -> None: def _train_rw(self, data: dict[str, Any]) -> None: """Train on a batch (reward model).""" + if _rw_loss_weight(data) == 0: + _log_empty_rw_stats(data["cu_seqlens"].device) self.engine.train() stats = self.engine.train_batch( input_=data, loss_fn=compute_rw_loss, - loss_weight_fn=lambda x: torch.tensor( - x["cu_seqlens"].shape[0] - 1, - dtype=torch.float, - device=current_platform.current_device(), - ), + loss_weight_fn=_rw_loss_weight, ) stats_tracker.scalar(**stats) @@ -41,15 +59,13 @@ def evaluate_rw(self, data: list[dict[str, Any]]) -> None: batched_call(self._evaluate_rw, data, unpack=False) def _evaluate_rw(self, data: dict[str, Any]) -> None: + if _rw_loss_weight(data) == 0: + _log_empty_rw_stats(data["cu_seqlens"].device) self.engine.eval() self.engine.eval_batch( input_=data, loss_fn=compute_rw_loss, - loss_weight_fn=lambda x: torch.tensor( - x["cu_seqlens"].shape[0] - 1, - dtype=torch.float, - device=current_platform.current_device(), - ), + loss_weight_fn=_rw_loss_weight, ) @@ -58,16 +74,27 @@ def train_rw(self, *args, **kwargs): self._custom_function_call("train_rw", *args, **kwargs) def evaluate_rw(self, *args, **kwargs): - self._custom_function_call("evaluate_rw", *args, **kwargs) + # rw_modeling_collate_fn produces 2 sequences (chosen + rejected) per + # example; group_size=2 keeps each pair on the same DP rank. + args, kwargs = self._pad_eval_dispatch_args(args, kwargs, group_size=2) + self._custom_function_call("evaluate_rw", *args, group_size=2, **kwargs) def compute_rw_loss(scores: torch.Tensor, input_: dict[str, Any]) -> torch.Tensor: + device = scores.device cu_seqlens = input_["cu_seqlens"] seqlens = (cu_seqlens[1:] - cu_seqlens[:-1]).cpu() - n_pairs = (cu_seqlens.shape[0] - 1) // 2 + valid_pairs = _rw_valid_pairs(input_) + if not valid_pairs.any(): + _log_empty_rw_stats(device) + return torch.zeros((), dtype=torch.float32, device=device) + + valid_pair_mask = valid_pairs.to(device=device) + terminal_indices = seqlens.cumsum(0).to(device=device) - 1 assert scores.shape[0] == seqlens.sum(), (scores.shape, seqlens.sum()) - scores = scores[seqlens.cumsum(0) - 1].view(-1, 2).float() + scores = scores[terminal_indices].view(-1, 2)[valid_pair_mask].float() + loss = -(torch.nn.functional.logsigmoid(scores[:, 0] - scores[:, 1])) logging_loss = loss.detach() loss = loss.mean() @@ -75,7 +102,7 @@ def compute_rw_loss(scores: torch.Tensor, input_: dict[str, Any]) -> torch.Tenso # Logging. with torch.no_grad(): stats_tracker.denominator( - n_pairs=torch.ones(n_pairs, dtype=torch.bool, device=scores.device), + n_pairs=torch.ones(scores.shape[0], dtype=torch.bool, device=device), ) stats_tracker.stat( correct_ratio=(scores[:, 0] > scores[:, 1]).detach().float(), diff --git a/areal/trainer/sft/lm_engine.py b/areal/trainer/sft/lm_engine.py index 48e3c23792..10883c4a0c 100644 --- a/areal/trainer/sft/lm_engine.py +++ b/areal/trainer/sft/lm_engine.py @@ -46,6 +46,7 @@ def train_lm(self, *args, **kwargs): self._custom_function_call("train_lm", *args, **kwargs) def evaluate_lm(self, *args, **kwargs): + args, kwargs = self._pad_eval_dispatch_args(args, kwargs, group_size=1) self._custom_function_call("evaluate_lm", *args, **kwargs) diff --git a/areal/utils/data.py b/areal/utils/data.py index 834ea23e97..2979f0bd7c 100644 --- a/areal/utils/data.py +++ b/areal/utils/data.py @@ -1639,3 +1639,36 @@ def _compute_approx_kl( if apply_clamp: log_ratio = log_ratio.clamp(min=-10, max=10) return log_ratio + + +def make_dummy_eval_item(template: dict[str, Any]) -> dict[str, Any]: + """Create a zero-contribution dummy item matching *template*'s schema. + + Every tensor field is replaced with a minimal all-zeros tensor that + preserves dtype and device. ``attention_mask`` and ``loss_mask`` are + set to zero so that downstream loss/metric code treats the item as + contributing nothing. + """ + + def _zero_tensor_like(tensor: torch.Tensor) -> torch.Tensor: + return torch.zeros((1, 1), dtype=tensor.dtype, device=tensor.device) + + dummy: dict[str, Any] = {} + for key, value in template.items(): + if key in {"attention_mask", "loss_mask"}: + if isinstance(value, torch.Tensor): + dummy[key] = _zero_tensor_like(value) + else: + dummy[key] = torch.zeros((1, 1), dtype=torch.bool) + continue + + if key.startswith("multi_modal_input"): + dummy[key] = [{}] + continue + + if isinstance(value, torch.Tensor): + dummy[key] = _zero_tensor_like(value) + else: + dummy[key] = copy.deepcopy(value) + + return dummy diff --git a/areal/utils/stats_logger.py b/areal/utils/stats_logger.py index 54e695b50c..228cc772c6 100644 --- a/areal/utils/stats_logger.py +++ b/areal/utils/stats_logger.py @@ -5,7 +5,6 @@ import swanlab import torch.distributed as dist -import trackio import wandb from tensorboardX import SummaryWriter @@ -96,6 +95,8 @@ def init(self): self._trackio_enabled = False trackio_config = self.config.trackio if trackio_config.mode != "disabled": + import trackio + trackio.init( project=trackio_config.project or self.config.experiment_name, name=trackio_config.name or self.config.trial_name, @@ -126,6 +127,8 @@ def close(self): wandb.finish() swanlab.finish() if getattr(self, "_trackio_enabled", False): + import trackio + trackio.finish() if self.summary_writer is not None: self.summary_writer.close() @@ -150,6 +153,8 @@ def commit(self, epoch: int, step: int, global_step: int, data: dict | list[dict wandb.log(item, step=log_step + i) swanlab.log(item, step=log_step + i) if getattr(self, "_trackio_enabled", False): + import trackio + trackio.log(item, step=log_step + i) if self.summary_writer is not None: for key, val in item.items(): diff --git a/docs/en/cli_reference.md b/docs/en/cli_reference.md index a01ab8ca85..d51c58866c 100644 --- a/docs/en/cli_reference.md +++ b/docs/en/cli_reference.md @@ -353,7 +353,7 @@ Configuration for PPO actor model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -420,7 +420,7 @@ Configuration for PPO critic model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -460,7 +460,7 @@ Core configuration for model training, including optimization and backend settin | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -1067,7 +1067,7 @@ Configuration class: TeacherConfig | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | diff --git a/docs/en/tutorial/installation.md b/docs/en/tutorial/installation.md index 22ac37dffa..72dd57f8c0 100644 --- a/docs/en/tutorial/installation.md +++ b/docs/en/tutorial/installation.md @@ -105,15 +105,34 @@ This installs CUDA-dependent training packages (Megatron, Torch Memory Saver) pl **SGLang** as the default inference backend. These packages require Linux x86_64 with CUDA 12.x and compatible NVIDIA drivers. +#### Using vLLM Instead of SGLang + +SGLang and vLLM pin mutually-incompatible `torch` / `torchao` versions, so they live in +separate pyproject files. The default `pyproject.toml` uses SGLang; for vLLM, use +`pyproject.vllm.toml`: + +```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock +uv sync --extra cuda +``` + +Alternatively, without replacing `pyproject.toml`: + +```bash +uv pip install -r pyproject.vllm.toml --extra cuda +``` + #### Flash Attention Pre-built Wheels -Flash Attention v2 is included in `--extra cuda` and `--extra cuda-vllm`, but PyPI only -ships source distributions that require CUDA compilation (~30 min). To skip compilation, -install a **pre-built wheel** before running `uv sync`. +Flash Attention v2 is included in `--extra cuda`, but PyPI only ships source +distributions that require CUDA compilation (~30 min). To skip compilation, install a +**pre-built wheel** before running `uv sync`. Flash Attention wheels are linked against a specific PyTorch version at compile time. -SGLang uses **torch 2.9** and vLLM uses **torch 2.10**, so you must pick the matching -wheel. Replace `cpXYZ` with your Python version (`cp311` for 3.11, `cp312` for 3.12). +SGLang (the default `pyproject.toml`) uses **torch 2.9** and vLLM +(`pyproject.vllm.toml`) uses **torch 2.10**, so you must pick the matching wheel. +Replace `cpXYZ` with your Python version (`cp311` for 3.11, `cp312` for 3.12). **SGLang** (default, torch 2.9): @@ -126,15 +145,17 @@ uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/rele uv sync --extra cuda ``` -**vLLM** (torch 2.10): +**vLLM** (torch 2.10, requires `pyproject.vllm.toml`): ```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock # Python 3.12 uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl" # Python 3.11 # uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl" -uv sync --extra cuda-vllm +uv sync --extra cuda ``` Browse all available wheels at @@ -147,25 +168,14 @@ is suitable only for development, testing, and non-GPU workflows. You can also install individual extras instead of the full `cuda` bundle: -- `sglang`: SGLang inference engine -- `vllm`: vLLM inference engine +- `sglang`: SGLang inference engine (in `pyproject.toml`) +- `vllm`: vLLM inference engine (in `pyproject.vllm.toml`) - `megatron`: Megatron training backend - `tms`: Torch Memory Saver - `flash-attn`: Flash Attention v2 - `kernels`: Hugging Face Kernels runtime -- `cuda-train`: Training packages only (megatron + tms, no inference backend) -- `cuda-sglang`: cuda-train + sglang + flash-attn -- `cuda-vllm`: cuda-train + vllm + flash-attn -- `cuda`: alias for cuda-sglang (default, backward-compatible) - -**Note**: You can mix and match individual extras: - -```bash -# vLLM with Hugging Face Kernels and flash-attn (no megatron, no tms) -uv sync --extra vllm --extra flash-attn --extra kernels -# vLLM with all training packages -uv sync --extra cuda-train --extra vllm -``` +- `cuda-train`: Training packages only (megatron + tms + kernels, no inference backend) +- `cuda`: cuda-train + inference backend + flash-attn ### Using Hugging Face Kernels in Training diff --git a/docs/zh/cli_reference.md b/docs/zh/cli_reference.md index f68accdcb5..afd64db4af 100644 --- a/docs/zh/cli_reference.md +++ b/docs/zh/cli_reference.md @@ -351,7 +351,7 @@ Configuration for PPO actor model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -418,7 +418,7 @@ Configuration for PPO critic model, a subclass of a TrainEngine. | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -458,7 +458,7 @@ Core configuration for model training, including optimization and backend settin | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | @@ -1065,7 +1065,7 @@ Configuration class: TeacherConfig | `fsdp` | [`FSDPEngineConfig`](section-fsdp-engine) | **Required** | - | | `archon` | [`ArchonEngineConfig`](section-archon-engine) | **Required** | - | | `megatron` | [`MegatronEngineConfig`](section-megatron-engine) | **Required** | - | -| `use_lora` | boolean | `False` | Whether to use LoRA. Supported by FSDP and Megatron (Megatron requires `megatron.bridge_type=megatron-bridge`). For rollout engines, enable LoRA in vLLM/SGLang as well. | +| `use_lora` | boolean | `False` | Whether to use LoRA. Only support FSDP. Note that should be enabled together with vLLM/SGLang. | | `lora_rank` | integer | `32` | lora rank | | `lora_alpha` | integer | `16` | lora alpha | | `target_modules` | list of string | **Required** | lora target_modules. | diff --git a/docs/zh/tutorial/installation.md b/docs/zh/tutorial/installation.md index da957fed17..eb7c7c9675 100644 --- a/docs/zh/tutorial/installation.md +++ b/docs/zh/tutorial/installation.md @@ -88,13 +88,31 @@ uv sync --extra cuda 这将安装 CUDA 依赖的训练包（Megatron、Torch Memory Saver）以及 **SGLang** 作为默认推理后端。这些包需要 Linux x86_64 和 CUDA 12.x 及兼容的 NVIDIA 驱动。 +#### 使用 vLLM 替代 SGLang + +SGLang 和 vLLM 锁定了互不兼容的 `torch` / `torchao` 版本，因此它们位于独立的 pyproject 文件中。默认的 +`pyproject.toml` 使用 SGLang；若需使用 vLLM，请使用 `pyproject.vllm.toml`： + +```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock +uv sync --extra cuda +``` + +也可以不替换 `pyproject.toml`： + +```bash +uv pip install -r pyproject.vllm.toml --extra cuda +``` + #### Flash Attention 预编译 Wheel -Flash Attention v2 包含在 `--extra cuda` 和 `--extra cuda-vllm` 中，但 PyPI 仅提供源码分发包，从源码编译耗时约 -30 分钟。为跳过编译，请在运行 `uv sync` **之前**安装**预编译 wheel**。 +Flash Attention v2 包含在 `--extra cuda` 中，但 PyPI 仅提供源码分发包，从源码编译耗时约 30 分钟。为跳过编译，请在运行 +`uv sync` **之前**安装**预编译 wheel**。 -Flash Attention wheel 在编译时与特定 PyTorch 版本绑定。SGLang 使用 **torch 2.9**， vLLM 使用 **torch -2.10**，请选择对应的 wheel。将 `cpXYZ` 替换为您的 Python 版本 （3.11 对应 `cp311`，3.12 对应 `cp312`）。 +Flash Attention wheel 在编译时与特定 PyTorch 版本绑定。SGLang（默认 `pyproject.toml`）使用 **torch +2.9**，vLLM（`pyproject.vllm.toml`）使用 **torch 2.10**，请选择对应的 wheel。将 `cpXYZ` 替换为您的 Python +版本（3.11 对应 `cp311`，3.12 对应 `cp312`）。 **SGLang**（默认，torch 2.9）： @@ -107,15 +125,17 @@ uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/rele uv sync --extra cuda ``` -**vLLM**（torch 2.10）： +**vLLM**（torch 2.10，需要 `pyproject.vllm.toml`）： ```bash +cp pyproject.vllm.toml pyproject.toml +cp uv.vllm.lock uv.lock # Python 3.12 uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp312-cp312-linux_x86_64.whl" # Python 3.11 # uv pip install "https://github.com/mjun0812/flash-attention-prebuild-wheels/releases/download/v0.7.16/flash_attn-2.8.3+cu128torch2.10-cp311-cp311-linux_x86_64.whl" -uv sync --extra cuda-vllm +uv sync --extra cuda ``` 浏览所有可用 wheel： 。 @@ -125,25 +145,14 @@ CUDA 的训练和推理功能将不可用。此配置仅适用于开发、测试 您也可以单独安装各个 extra，而不是完整的 `cuda` 捆绑包： -- `sglang`：SGLang 推理引擎 -- `vllm`：vLLM 推理引擎 +- `sglang`：SGLang 推理引擎（在 `pyproject.toml` 中） +- `vllm`：vLLM 推理引擎（在 `pyproject.vllm.toml` 中） - `megatron`：Megatron 训练后端 - `tms`：Torch Memory Saver - `flash-attn`：Flash Attention v2 - `kernels`：Hugging Face Kernels 运行时 -- `cuda-train`：仅训练包（megatron + tms，不含推理后端） -- `cuda-sglang`：cuda-train + sglang + flash-attn -- `cuda-vllm`：cuda-train + vllm + flash-attn -- `cuda`：cuda-sglang 的别名（默认，向后兼容） - -**注意**：您可以混合搭配各个 extra： - -```bash -# vLLM 带 Hugging Face Kernels 和 flash-attn（不含 megatron 和 tms） -uv sync --extra vllm --extra flash-attn --extra kernels -# vLLM 加所有训练包 -uv sync --extra cuda-train --extra vllm -``` +- `cuda-train`：仅训练包（megatron + tms + kernels，不含推理后端） +- `cuda`：cuda-train + 推理后端 + flash-attn ### 在训练中使用 Hugging Face Kernels diff --git a/pyproject.toml b/pyproject.toml index c9844e288d..3d0bb2564f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -139,16 +139,14 @@ dependencies = [ [project.optional-dependencies] # CUDA-dependent extras - these packages only have Linux x86_64 wheels # Platform markers ensure they're skipped on unsupported platforms +# +# NOTE: vLLM has a separate pyproject file (pyproject.vllm.toml) because +# sglang and vllm pin mutually-incompatible torch / torchao versions. +# See pyproject.vllm.toml for the vLLM variant. sglang = [ "sglang[tracing]==0.5.9; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torch==2.9.1+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", "nvidia-cudnn-cu12==9.16.0.29; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", -] -vllm = [ - "vllm==0.17.0; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torch==2.10.0+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", ] @@ -167,35 +165,17 @@ megatron = [ "megatron-bridge==0.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'", ] # Convenience extra for CUDA training packages (no inference backend) -# Install with --extra sglang or --extra vllm for a complete setup cuda-train = [ "areal[tms]", "areal[megatron]", "areal[kernels]", ] -# Backward-compatible: `uv sync --extra cuda` installs training packages + sglang. -# For vllm instead, use: `uv sync --extra cuda-vllm` -cuda-sglang = [ +# Full CUDA setup: training packages + SGLang inference + flash-attn +cuda = [ "areal[cuda-train]", "areal[sglang]", "areal[flash-attn]", - "torch==2.9.1+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", - "nvidia-cudnn-cu12==9.16.0.29; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", -] -cuda-vllm = [ - "areal[cuda-train]", - "areal[vllm]", - "areal[flash-attn]", - "torch==2.10.0+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", - "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", -] -cuda = [ - "areal[cuda-sglang]", - "torch==2.9.1+cu129; sys_platform == 'linux' and platform_machine == 'x86_64'", - "nvidia-cudnn-cu12==9.16.0.29; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", ] @@ -242,40 +222,11 @@ environments = [ "sys_platform == 'darwin' and platform_machine == 'arm64'", "sys_platform == 'darwin' and platform_machine == 'x86_64'", ] -conflicts = [ - [ - { extra = "sglang" }, - { extra = "vllm" }, - ], - # cuda includes sglang, so it also conflicts with vllm - [ - { extra = "cuda-sglang" }, - { extra = "vllm" }, - ], - [ - { extra = "cuda" }, - { extra = "vllm" }, - ], - # cuda-vllm includes vllm, so it conflicts with sglang and cuda - [ - { extra = "cuda-vllm" }, - { extra = "sglang" }, - ], - [ - { extra = "cuda-vllm" }, - { extra = "cuda" }, - ], - [ - { extra = "cuda-vllm" }, - { extra = "cuda-sglang" }, - ], -] override-dependencies = [ # litellm[proxy] conflicts with sglang's exact pins: # - litellm requires openai>=2.8.0, sglang pins openai==2.6.1 # - litellm requires soundfile>=0.12.1,<0.13.0, sglang pins soundfile==0.13.1 # sglang pins torchao==0.9.0, but we require >=0.15.0 for archon fp8 - # the specific version (0.15.0 or 0.16.0) is controlled by extras # nvidia-cudnn-cu12 is required for SGLang Vision Models "openai>=2.8.0", "soundfile>=0.12.1,<0.13.0", @@ -288,11 +239,11 @@ override-dependencies = [ "mamba-ssm; sys_platform == 'never'", "causal-conv1d; sys_platform == 'never'", "flash-linear-attention; sys_platform == 'never'", - + # Version conflicts with megatron-bridge (resorting to the existing version) - "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", # Prevent megatron-bridge from installing megatron-core[dev,mlm] extras + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", "hydra-core==1.4.0.dev1", - "timm==1.0.16", + "timm==1.0.16", ] # Static metadata so uv lock resolves flash-attn without downloading or building. @@ -313,24 +264,15 @@ explicit = true [tool.uv.sources] torch = [ { index = "pytorch-cu129", extra = "sglang" }, - { index = "pytorch-cu129", extra = "vllm" }, { index = "pytorch-cu129", extra = "cuda" }, - { index = "pytorch-cu129", extra = "cuda-sglang" }, - { index = "pytorch-cu129", extra = "cuda-vllm" }, ] torchaudio = [ { index = "pytorch-cu129", extra = "sglang" }, - { index = "pytorch-cu129", extra = "vllm" }, { index = "pytorch-cu129", extra = "cuda" }, - { index = "pytorch-cu129", extra = "cuda-sglang" }, - { index = "pytorch-cu129", extra = "cuda-vllm" }, ] torchvision = [ { index = "pytorch-cu129", extra = "sglang" }, - { index = "pytorch-cu129", extra = "vllm" }, { index = "pytorch-cu129", extra = "cuda" }, - { index = "pytorch-cu129", extra = "cuda-sglang" }, - { index = "pytorch-cu129", extra = "cuda-vllm" }, ] # ============================================================================= diff --git a/pyproject.vllm.toml b/pyproject.vllm.toml new file mode 100644 index 0000000000..10bfefbf02 --- /dev/null +++ b/pyproject.vllm.toml @@ -0,0 +1,328 @@ +# vLLM variant of the AReaL project configuration. +# +# sglang and vllm pin mutually-incompatible torch / torchao versions, +# so they live in separate pyproject files to avoid resolution conflicts. +# The default pyproject.toml uses SGLang; this file uses vLLM. +# +# Usage (custom environment): +# cp pyproject.vllm.toml pyproject.toml +# uv sync --extra cuda +# +# Usage (Docker): +# docker build --build-arg VARIANT=vllm -t areal-runtime:dev-vllm . +# +# Usage (pip-install without replacing pyproject.toml): +# uv pip install -r pyproject.vllm.toml --extra cuda + +[build-system] +requires = ["uv_build>=0.9.18,<0.10.0"] +build-backend = "uv_build" + +[project] +name = "areal" +description = "AReaL: A Large-Scale Asynchronous Reinforcement Learning System" +readme = "README.md" +license = {text = "Apache-2.0"} +requires-python = ">=3.11,<3.13" +version = "1.0.2" +authors = [ + {name = "AReaL Team"}, +] +maintainers = [ + {name = "AReaL Team"}, +] +keywords = [ + "distributed-systems", + "reinforcement-learning", + "large-language-models", + "llm-training", + "llm-agent", + "agentic-rl", +] +classifiers = [ + # 3 - Alpha + # 4 - Beta + # 5 - Production/Stable + "Development Status :: 2 - Pre-Alpha", + "Environment :: GPU :: NVIDIA CUDA :: 12", + "Intended Audience :: Developers", + "Intended Audience :: Science/Research", + "License :: OSI Approved :: Apache Software License", + "Operating System :: POSIX :: Linux", + "Programming Language :: Python :: 3", + "Programming Language :: Python :: 3.11", + "Programming Language :: Python :: 3.12", + "Topic :: Scientific/Engineering :: Artificial Intelligence", + "Topic :: System :: Distributed Computing", +] + +dependencies = [ + # Core ML/AI libraries + "torch>=2.10.0,<2.11; sys_platform != 'darwin' or platform_machine != 'x86_64'", + "torch<2.9.1; sys_platform == 'darwin' and platform_machine == 'x86_64'", + "torchaudio", + "torchvision", + "torchdata", + "torchao==0.16.0", + "huggingface_hub", + "datasets>=3.0.0", + "transformers==4.57.1", + "peft", + "qwen_agent", + "openai-agents", + "anthropic", + "claude-agent-sdk", + "litellm[proxy]>=1.81.3", + "openhands", + "langchain", + "langchain-openai", + + # Visualization + "pandas", + "matplotlib", + "seaborn", + + # Utilities and data processing + "pillow>=12.1.1", + "pylatexenc", + "zstandard", + "setproctitle", + "nltk", + "sentencepiece", + "einops", + "orjson", + "tqdm", + "rich", + "pydantic", + "PyYAML", + "omegaconf==2.4.0.dev2", + "hydra-core==1.4.0.dev1", + "packaging", + "lark", + "tabulate", + "pybase64", + "msgspec", + "math-verify==0.8.0", + "python-dotenv", + "json5", + "psutil", + "nvidia-ml-py; sys_platform == 'linux'", + "ninja", + "numba", + "blosc", + "pybind11>=2.10.0", + "networkx==3.3", + "aiofiles", + "aiohttp>=3.13.3,<4", + "httpx>=0.28.1", + "pyzmq", + "regex", + "python_dateutil", + "word2number", + "pebble", + "timeout-decorator", + "prettytable", + "h5py", + "mathruler==0.1.0", + + # Monitoring and logging + "wandb", + "tensorboardx", + "trackio", + "colorama", + "colorlog", + "swanboard==0.1.9b1", + "swanlab[dashboard]==0.6.12", + + # Distributed computing + "ray[default]", + "redis", + + # Web frameworks + "fastapi>=0.115.12", + "uvicorn", + "uvloop>=0.21.0", + "flask", + "tenacity", + + # Build and packaging tools + "build>=1.2.1", + "wheel>=0.43.0", + "cookiecutter>2.1.1", + "distro-info>=1.0", + "python-debian>=0.1.49", +] + +[project.optional-dependencies] +# CUDA-dependent extras - these packages only have Linux x86_64 wheels +# Platform markers ensure they're skipped on unsupported platforms +vllm = [ + "vllm==0.17.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", +] +tms = [ + "torch_memory_saver==0.0.9; sys_platform == 'linux'", +] +flash-attn = [ + "flash-attn==2.8.3; sys_platform == 'linux' and platform_machine == 'x86_64'", +] +kernels = [ + "kernels==0.12.2", +] +megatron = [ + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "mbridge==0.15.1; sys_platform == 'linux' and platform_machine == 'x86_64'", + "megatron-bridge==0.3.0; sys_platform == 'linux' and platform_machine == 'x86_64'", +] +cuda-train = [ + "areal[tms]", + "areal[megatron]", + "areal[kernels]", +] +# Full CUDA setup: training packages + vLLM inference + flash-attn +cuda = [ + "areal[cuda-train]", + "areal[vllm]", + "areal[flash-attn]", + "torch; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchaudio; sys_platform == 'linux' and platform_machine == 'x86_64'", + "torchvision; sys_platform == 'linux' and platform_machine == 'x86_64'", +] + +[project.urls] +"Homepage" = "https://github.com/inclusionAI/AReaL" +"Repository" = "https://github.com/inclusionAI/AReaL" +"Documentation" = "https://inclusionai.github.io/AReaL/en/intro.html" +"Bug Tracker" = "https://github.com/inclusionAI/AReaL/issues" + +[dependency-groups] +dev = [ + "pytest", + "pytest-asyncio", + "ipython", + "ruff==0.14.9", + "clang-format==19.1.7", + "sh", + "mdformat==0.7.17", + "mdformat-gfm", + "mdformat-tables", + "mdformat-frontmatter", + "plotly", + "pre-commit", + "sphinx", + "sphinx-nefertiti", + "jupyter-book==1.0.4.post1", +] + +# ============================================================================= +# Tool configurations: uv +# ============================================================================= + +[tool.uv.build-backend] +module-root = "" + +[tool.uv] +environments = [ + "sys_platform == 'linux' and platform_machine == 'x86_64'", + "sys_platform == 'linux' and platform_machine == 'aarch64'", + "sys_platform == 'darwin' and platform_machine == 'arm64'", + "sys_platform == 'darwin' and platform_machine == 'x86_64'", +] +override-dependencies = [ + # Required by megatron-bridge but are optional (as per docs) cuda-based installs + "transformer-engine; sys_platform == 'never'", + "nv-grouped-gemm; sys_platform == 'never'", + "mamba-ssm; sys_platform == 'never'", + "causal-conv1d; sys_platform == 'never'", + "flash-linear-attention; sys_platform == 'never'", + + # Version conflicts with megatron-bridge (resorting to the existing version) + "megatron-core==0.16.0; sys_platform == 'linux' and platform_machine == 'x86_64'", + "hydra-core==1.4.0.dev1", + "timm==1.0.16", +] + +[[tool.uv.dependency-metadata]] +name = "flash-attn" +version = "2.8.3" +requires-dist = ["torch", "einops"] + +[tool.uv.extra-build-dependencies] +flash-attn = [{ requirement = "torch", match-runtime = true }] + +[[tool.uv.index]] +name = "pytorch-cu129" +url = "https://download.pytorch.org/whl/cu129" +explicit = true + +[tool.uv.sources] +torch = [ + { index = "pytorch-cu129", extra = "vllm" }, + { index = "pytorch-cu129", extra = "cuda" }, +] +torchaudio = [ + { index = "pytorch-cu129", extra = "vllm" }, + { index = "pytorch-cu129", extra = "cuda" }, +] +torchvision = [ + { index = "pytorch-cu129", extra = "vllm" }, + { index = "pytorch-cu129", extra = "cuda" }, +] + +# ============================================================================= +# Tool configurations: pytest +# ============================================================================= + +[tool.pytest.ini_options] +pythonpath = ["."] +filterwarnings = [ + "ignore::DeprecationWarning", + "ignore::PendingDeprecationWarning", + "ignore::FutureWarning", + "ignore::UserWarning:torch.*", + "ignore::UserWarning:transformers.*", +] +markers = [ + "slow: mark test as slow, expected to cost more than 30 seconds and will not run in CI by default.", + "ci: mark test as must-run in CI (only marked for slow tests).", + "gpu: mark test that uses a single GPU", + "multi_gpu: mark test that uses more than one GPU", + "sglang: mark test that requires the SGLang inference backend", + "vllm: mark test that requires the vLLM inference backend", +] + +# ============================================================================= +# Tool configurations: ruff +# ============================================================================= + +[tool.ruff] +line-length = 88 +target-version = "py311" + +[tool.ruff.lint] +select = [ + "E", # pycodestyle errors + "W", # pycodestyle warnings + "F", # pyflakes + "I", # isort + "UP", # pyupgrade +] +ignore = [ + "E501", # E501 Line too long +] + +[tool.ruff.lint.isort] +section-order = [ + "future", + "standard-library", + "third-party", + "first-party", + "areal", + "local-folder", +] +from-first = false + +[tool.ruff.lint.isort.sections] +"areal" = ["areal"] diff --git a/tests/test_eval_dispatch.py b/tests/test_eval_dispatch.py new file mode 100644 index 0000000000..ec96af3659 --- /dev/null +++ b/tests/test_eval_dispatch.py @@ -0,0 +1,434 @@ +from types import MethodType, SimpleNamespace +from typing import Any, cast +from unittest.mock import patch + +import pytest +import torch +import torch.distributed as dist +import torch.nn.functional as F + +from areal.api.cli_args import MicroBatchSpec +from areal.engine.core.train_engine import compute_total_loss_weight +from areal.infra.controller.train_controller import ( + _dispatch_tensors, + _pad_eval_batch, +) +from areal.trainer.rw.rw_engine import ( + RWController, + RWEngine, + _rw_loss_weight, + compute_rw_loss, +) +from areal.trainer.sft.lm_engine import LMController +from areal.utils.data import ( + MicroBatchList, + concat_padded_tensors, + make_dummy_eval_item, + split_padded_tensor_dict_into_mb_list, +) +from areal.utils.stats_tracker import DistributedStatsTracker + + +def _make_item(idx: int, seqlen: int = 2) -> dict[str, object]: + return { + "input_ids": torch.full((1, seqlen), idx + 1, dtype=torch.long), + "attention_mask": torch.ones((1, seqlen), dtype=torch.bool), + "loss_mask": torch.ones((1, seqlen), dtype=torch.bool), + "meta": {"id": idx}, + } + + +def _flatten_splits(splits: list[list[dict[str, object]]]) -> list[dict[str, object]]: + return [item for group in splits for item in group] + + +def _count_dummies(items: list[dict[str, object]]) -> int: + return sum( + int(cast(torch.Tensor, item["attention_mask"]).sum().item() == 0) + for item in items + ) + + +def _make_rw_pair( + pair_idx: int, chosen_len: int = 3, rejected_len: int = 2 +) -> tuple[dict[str, object], dict[str, object]]: + chosen: dict[str, object] = { + "input_ids": torch.full((1, chosen_len), pair_idx * 2 + 1, dtype=torch.long), + "attention_mask": torch.ones((1, chosen_len), dtype=torch.bool), + "meta": {"pair": pair_idx, "role": "chosen"}, + } + rejected: dict[str, object] = { + "input_ids": torch.full((1, rejected_len), pair_idx * 2 + 2, dtype=torch.long), + "attention_mask": torch.ones((1, rejected_len), dtype=torch.bool), + "meta": {"pair": pair_idx, "role": "rejected"}, + } + return chosen, rejected + + +def _build_rw_batch(n_pairs: int, chosen_len: int = 3, rejected_len: int = 2): + items: list[dict[str, object]] = [] + for p in range(n_pairs): + c, r = _make_rw_pair(p, chosen_len, rejected_len) + items.extend([c, r]) + return items + + +def _make_rw_input(seqlens: list[int]) -> dict[str, torch.Tensor]: + cu_seqlens = [0] + for seqlen in seqlens: + cu_seqlens.append(cu_seqlens[-1] + seqlen) + return {"cu_seqlens": torch.tensor(cu_seqlens, dtype=torch.int32)} + + +class TestEvalBatchPadding: + def test_pad_eval_batch_no_padding_when_divisible(self): + items = [_make_item(i) for i in range(8)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 8 + assert _count_dummies(padded) == 0 + + def test_pad_eval_batch_pads_when_not_divisible(self): + items = [_make_item(i) for i in range(7)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 8 + assert _count_dummies(padded) == 1 + + def test_pad_eval_batch_pads_when_n_less_than_dp(self): + items = [_make_item(i) for i in range(2)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 4 + assert _count_dummies(padded) == 2 + + def test_dispatch_tensors_raises_when_not_divisible(self): + items = [_make_item(i) for i in range(7)] + with pytest.raises(ValueError, match="divisible"): + _dispatch_tensors(items, dp_size=4) + + def test_pad_then_dispatch_end_to_end(self): + items = [_make_item(i) for i in range(7)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + splits, _ = _dispatch_tensors(padded, dp_size=4) + assert all(len(group) == 2 for group in splits) + assert _count_dummies(_flatten_splits(splits)) == 1 + + def test_make_dummy_eval_item_schema(self): + template: dict[str, object] = { + "input_ids": torch.tensor([[2, 3, 4]], dtype=torch.long), + "attention_mask": torch.tensor([[True, True, False]], dtype=torch.bool), + "loss_mask": torch.tensor([[1, 1, 0]], dtype=torch.int32), + "multi_modal_input": [{"image": torch.tensor([1.0])}], + "meta": {"tag": ["x"]}, + } + + dummy = make_dummy_eval_item(template) + assert set(dummy.keys()) == set(template.keys()) + assert dummy["multi_modal_input"] == [{}] + torch.testing.assert_close( + dummy["attention_mask"], + torch.zeros((1, 1), dtype=torch.bool), + rtol=0.0, + atol=0.0, + ) + torch.testing.assert_close( + dummy["loss_mask"], + torch.zeros((1, 1), dtype=cast(torch.Tensor, template["loss_mask"]).dtype), + rtol=0.0, + atol=0.0, + ) + torch.testing.assert_close( + dummy["input_ids"], + torch.zeros((1, 1), dtype=cast(torch.Tensor, template["input_ids"]).dtype), + rtol=0.0, + atol=0.0, + ) + cast(dict[str, list[str]], template["meta"])["tag"].append("y") + assert dummy["meta"] == {"tag": ["x"]} + + def test_pad_eval_batch_keeps_multimodal_payload_aligned(self): + items: list[dict[str, object]] = [] + for _ in range(3): + items.append( + { + "input_ids": torch.tensor([[11, 12, 13]], dtype=torch.long), + "attention_mask": torch.tensor( + [[True, True, True]], dtype=torch.bool + ), + "loss_mask": torch.tensor([[1, 1, 1]], dtype=torch.int32), + "multi_modal_input": [ + { + "pixel_values": torch.ones((1, 2, 2), dtype=torch.float32), + "image_grid_thw": torch.tensor( + [[1, 1, 1]], dtype=torch.int32 + ), + } + ], + } + ) + + (padded,) = _pad_eval_batch((items,), dp_size=4) + batched = concat_padded_tensors(padded) + mb_list = split_padded_tensor_dict_into_mb_list(batched, MicroBatchSpec()) + + assert len(padded) == 4 + assert len(batched["multi_modal_input"]) == batched["attention_mask"].shape[0] + assert padded[-1]["multi_modal_input"] == [{}] + for mb in mb_list.mbs: + assert len(mb["multi_modal_input"]) == mb["attention_mask"].shape[0] + + def test_pad_eval_batch_group_size_odd_dp(self): + items = [_make_item(i) for i in range(2)] + (padded,) = _pad_eval_batch((items,), dp_size=3, group_size=2) + assert len(padded) == 6 + assert len(padded) % 2 == 0 + assert len(padded) % 3 == 0 + assert _count_dummies(padded) == 4 + + def test_pad_eval_batch_group_size_even_dp(self): + items = [_make_item(i) for i in range(6)] + (padded,) = _pad_eval_batch((items,), dp_size=4, group_size=2) + assert len(padded) == 8 + assert len(padded) % 2 == 0 + assert len(padded) % 4 == 0 + assert _count_dummies(padded) == 2 + + def test_pad_eval_batch_group_size_no_pad_needed(self): + items = [_make_item(i) for i in range(6)] + (padded,) = _pad_eval_batch((items,), dp_size=3, group_size=2) + assert len(padded) == 6 + assert _count_dummies(padded) == 0 + + def test_pad_eval_batch_default_group_size_unchanged(self): + items = [_make_item(i) for i in range(7)] + (padded,) = _pad_eval_batch((items,), dp_size=4) + assert len(padded) == 8 + assert _count_dummies(padded) == 1 + + def test_compute_total_loss_weight_allows_local_zero( + self, monkeypatch: pytest.MonkeyPatch + ): + mb_list = MicroBatchList( + data={}, + mb_spec=MicroBatchSpec(), + mbs=[{"attention_mask": torch.zeros((1, 1), dtype=torch.bool)}], + group_lens=[1], + ) + + def _mock_all_reduce( + tensor: torch.Tensor, group: dist.ProcessGroup | None = None + ): + del group + tensor.add_(3.0) + + monkeypatch.setattr(dist, "all_reduce", _mock_all_reduce) + + total_weight = compute_total_loss_weight( + mb_list=mb_list, + loss_weight_fn=lambda _mb: torch.tensor(0.0), + dp_group=cast(dist.ProcessGroup, object()), + ) + + torch.testing.assert_close(total_weight, torch.tensor(3.0), rtol=0.0, atol=0.0) + + +class TestRWDispatchGrouping: + def test_dispatch_group_size_preserves_rw_pairs(self): + dp_size = 4 + items = _build_rw_batch(n_pairs=8) + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=2) + assert len(padded) == 16 + splits, _ = _dispatch_tensors(padded, dp_size=dp_size, group_size=2) + + for shard_items in splits: + assert len(shard_items) % 2 == 0, ( + f"Shard received odd item count {len(shard_items)}" + ) + for j in range(0, len(shard_items), 2): + c_meta = shard_items[j].get("meta", {}) + r_meta = shard_items[j + 1].get("meta", {}) + if c_meta.get("pair") is not None: + assert c_meta["pair"] == r_meta["pair"], ( + f"Pair split across items: {c_meta} vs {r_meta}" + ) + assert c_meta["role"] == "chosen" + assert r_meta["role"] == "rejected" + + def test_dispatch_group_size_rw_5_examples_dp4(self): + dp_size = 4 + items = _build_rw_batch(n_pairs=5) + assert len(items) == 10 + + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=2) + assert len(padded) == 16 + assert len(padded) % (dp_size * 2) == 0 + + splits, _ = _dispatch_tensors(padded, dp_size=dp_size, group_size=2) + assert len(splits) == dp_size + for shard_items in splits: + assert len(shard_items) % 2 == 0, ( + f"Shard received odd item count {len(shard_items)}, view(-1, 2) would fail" + ) + + def test_dispatch_group_size_not_divisible_raises(self): + items = _build_rw_batch(n_pairs=3) + items.append(items[0]) + with pytest.raises(ValueError, match="divisible by group_size"): + _dispatch_tensors(items, dp_size=2, group_size=2) + + def test_dispatch_group_size_1_unchanged(self): + items = [_make_item(i, seqlen=i + 1) for i in range(8)] + _, indices_default = _dispatch_tensors(items, dp_size=4) + _, indices_gs1 = _dispatch_tensors(items, dp_size=4, group_size=1) + assert indices_default == indices_gs1 + + @pytest.mark.parametrize( + "dp_size, group_size, n_items", + [ + (3, 2, 12), + (4, 2, 16), + (6, 2, 24), + (4, 3, 24), + (3, 2, 6), + (4, 2, 8), + ], + ) + def test_pad_and_dispatch_group_matrix( + self, dp_size: int, group_size: int, n_items: int + ): + items = [_make_item(i, seqlen=i % 5 + 1) for i in range(n_items)] + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=group_size) + assert len(padded) % (dp_size * group_size) == 0 + + splits, _ = _dispatch_tensors(padded, dp_size=dp_size, group_size=group_size) + assert len(splits) == dp_size + for shard in splits: + assert len(shard) % group_size == 0 + + @pytest.mark.parametrize( + "dp_size, group_size, n_raw", + [ + (3, 2, 5), + (4, 2, 7), + (4, 3, 10), + (6, 2, 3), + ], + ) + def test_pad_aligns_non_divisible_input( + self, dp_size: int, group_size: int, n_raw: int + ): + items = [_make_item(i) for i in range(n_raw)] + (padded,) = _pad_eval_batch((items,), dp_size=dp_size, group_size=group_size) + target = dp_size * group_size + assert len(padded) % target == 0 + assert len(padded) >= n_raw + + +class TestRWDummyPairSemantics: + def test_rw_loss_weight_counts_only_valid_pairs(self): + input_data = _make_rw_input([5, 4, 0, 0]) + + loss_weight = _rw_loss_weight(input_data) + + torch.testing.assert_close(loss_weight, torch.tensor(1.0), rtol=0.0, atol=0.0) + + def test_compute_rw_loss_ignores_dummy_pairs_in_loss_and_metrics(self): + tracker = DistributedStatsTracker() + input_data = _make_rw_input([5, 4, 0, 0]) + scores = torch.tensor([0.0, 0.0, 0.0, 0.0, 3.0, 0.0, 0.0, 0.0, 1.0]) + expected_loss = -F.logsigmoid(torch.tensor(2.0)) + + with patch("areal.trainer.rw.rw_engine.stats_tracker", tracker): + loss = compute_rw_loss(scores, input_data) + + stats = tracker.export(reset=True) + + torch.testing.assert_close(loss, expected_loss, rtol=1e-5, atol=1e-6) + assert stats["n_pairs"] == 1.0 + assert stats["correct_ratio/avg"] == 1.0 + assert stats["pos_score/avg"] == 3.0 + assert stats["neg_score/avg"] == 1.0 + torch.testing.assert_close( + torch.tensor(stats["loss/avg"]), + expected_loss, + rtol=1e-5, + atol=1e-6, + ) + + def test_compute_rw_loss_returns_zero_for_all_dummy_pairs(self): + tracker = DistributedStatsTracker() + input_data = _make_rw_input([0, 0]) + + with patch("areal.trainer.rw.rw_engine.stats_tracker", tracker): + loss = compute_rw_loss(torch.tensor([], dtype=torch.float32), input_data) + + stats = tracker.export(reset=True) + + torch.testing.assert_close(loss, torch.tensor(0.0), rtol=0.0, atol=0.0) + assert stats["n_pairs"] == 0.0 + assert "loss/avg" not in stats + + +class TestExplicitEvalDispatchControllers: + def test_lm_controller_evaluate_lm_explicitly_pads_batch(self): + controller = LMController.__new__(LMController) + cast(Any, controller).train_alloc = SimpleNamespace( + parallel=SimpleNamespace(dp_size=4) + ) + captured: dict[str, Any] = {} + + def _capture_call(self, method: str, *args, **kwargs): + captured["method"] = method + captured["args"] = args + captured["kwargs"] = kwargs + return None + + controller._custom_function_call = MethodType(_capture_call, controller) + + items = [_make_item(i) for i in range(7)] + controller.evaluate_lm(items) + + assert captured["method"] == "evaluate_lm" + padded_items = cast(list[dict[str, object]], captured["args"][0]) + assert len(padded_items) == 8 + assert captured["kwargs"] == {} + + def test_rw_controller_evaluate_rw_explicitly_pads_pairs(self): + controller = RWController.__new__(RWController) + cast(Any, controller).train_alloc = SimpleNamespace( + parallel=SimpleNamespace(dp_size=4) + ) + captured: dict[str, Any] = {} + + def _capture_call(self, method: str, *args, **kwargs): + captured["method"] = method + captured["args"] = args + captured["kwargs"] = kwargs + return None + + controller._custom_function_call = MethodType(_capture_call, controller) + + items = _build_rw_batch(n_pairs=5) + controller.evaluate_rw(items) + + assert captured["method"] == "evaluate_rw" + assert captured["kwargs"]["group_size"] == 2 + padded_items = cast(list[dict[str, object]], captured["args"][0]) + assert len(padded_items) == 16 + + def test_evaluate_rw_logs_zero_pair_denominator_for_all_dummy_local_batch(self): + tracker = DistributedStatsTracker() + + class _DummyEngine: + def eval(self) -> None: + return None + + def eval_batch(self, **kwargs) -> None: + del kwargs + return None + + with patch("areal.trainer.rw.rw_engine.stats_tracker", tracker): + RWEngine(_DummyEngine())._evaluate_rw(_make_rw_input([0, 0])) + + stats = tracker.export(reset=True) + + assert stats["n_pairs"] == 0.0 + assert "loss/avg" not in stats diff --git a/tests/test_fsdp_grad.py b/tests/test_fsdp_grad.py index bed14750dd..519bc9088d 100644 --- a/tests/test_fsdp_grad.py +++ b/tests/test_fsdp_grad.py @@ -112,8 +112,17 @@ def mock_process_groups(self): return dp_group, mp_group def test_empty_grads_returns_zero(self, mock_process_groups): + # Empty grads must still participate in all_reduce (e.g. LoRA frozen ranks) + # so that ranks with real grads don't hang. dp_group, mp_group = mock_process_groups - result = get_grad_norm_fp32([], dp_group, mp_group) + with patch("torch.distributed.all_reduce") as mock_allreduce: + result = get_grad_norm_fp32([], dp_group, mp_group) + assert result == 0.0 + assert mock_allreduce.call_count == 2 # called for dp_group and mp_group + + def test_empty_grads_participates_in_allreduce_no_groups(self): + # With no process groups, empty grads should still return 0.0 without hanging. + result = get_grad_norm_fp32([], None, None) assert result == 0.0 @pytest.mark.parametrize("norm_type", [1.0, 2.0, 3.0, float("inf")]) diff --git a/uv.lock b/uv.lock index 41299677b2..674263e5d6 100644 --- a/uv.lock +++ b/uv.lock @@ -17,25 +17,6 @@ supported-markers = [ "platform_machine == 'arm64' and sys_platform == 'darwin'", "platform_machine == 'x86_64' and sys_platform == 'darwin'", ] -conflicts = [[ - { package = "areal", extra = "sglang" }, - { package = "areal", extra = "vllm" }, -], [ - { package = "areal", extra = "cuda-sglang" }, - { package = "areal", extra = "vllm" }, -], [ - { package = "areal", extra = "cuda" }, - { package = "areal", extra = "vllm" }, -], [ - { package = "areal", extra = "cuda-vllm" }, - { package = "areal", extra = "sglang" }, -], [ - { package = "areal", extra = "cuda" }, - { package = "areal", extra = "cuda-vllm" }, -], [ - { package = "areal", extra = "cuda-sglang" }, - { package = "areal", extra = "cuda-vllm" }, -]] [manifest] overrides = [ @@ -72,16 +53,15 @@ name = "accelerate" version = "1.13.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "huggingface-hub", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "numpy", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "packaging", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform 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and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "aiofiles", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "aiohttp", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "anthropic", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "blosc", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "build", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "claude-agent-sdk", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = 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or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "distro-info", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "einops", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "fastapi", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "flask", 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sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "psutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pybase64", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pybind11", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 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sys_platform == 'linux')" }, + { name = "qwen-agent", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ray", version = "2.49.2", source = { registry = "https://pypi.org/simple" }, extra = ["default"], marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "ray", version = "2.54.0", source = { registry = "https://pypi.org/simple" }, extra = ["default"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "redis", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "regex", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "rich", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "seaborn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sentencepiece", marker = "(platform_machine == 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== 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tabulate", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tenacity", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tensorboardx", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "timeout-decorator", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torch", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang')" }, + { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, { name = "torchaudio", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchaudio", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchdata", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchvision", version = "0.17.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, { name = "torchvision", version = "0.24.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchvision", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "tqdm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "trackio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "transformers", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvicorn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvloop", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wandb", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wheel", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "word2number", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "zstandard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, ] -cuda-sglang = [ + +[package.optional-dependencies] +cuda = [ { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "sglang", extra = ["tracing"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda' and extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (extra != 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "sglang", extra = ["tracing"], marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "torchaudio", version = "2.9.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torchvision", version = "0.24.1+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] cuda-train = [ - { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "mbridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-bridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-core", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "torch-memory-saver", marker = "(platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, -] -cuda-vllm = [ - { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, - { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] flash-attn = [ - { name = "flash-attn", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] kernels = [ - { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'darwin' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, ] megatron = [ - { name = "mbridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-bridge", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "megatron-core", marker = "(platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, ] sglang = [ { name = "nvidia-cudnn-cu12", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "sglang", extra = ["tracing"], marker = "(platform_machine == 'x86_64' and sys_platform == 'linux' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (extra == 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== 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (platform_machine != 'aarch64' and platform_machine != 'x86_64' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, -] -vllm = [ - { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, - { name = "torchvision", version = "0.25.0+cu129", source = { registry = 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or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ruff", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sh", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sphinx", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sphinx-nefertiti", 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'extra-5-areal-vllm')" }, + { name = "aiohttp", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "aiohttp-cors", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "colorful", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "grpcio", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "opencensus", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "opentelemetry-exporter-prometheus", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "opentelemetry-proto", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "opentelemetry-sdk", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "prometheus-client", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "py-spy", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "pydantic", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "requests", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "smart-open", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "virtualenv", marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, ] [[package]] @@ -6140,45 +4991,40 @@ resolution-markers = [ "python_full_version < '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin'", ] dependencies = [ - { name = "click", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and 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(sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "opencensus", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform != 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extra == 'extra-5-areal-cuda-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-vllm') or (sys_platform == 'linux' and extra == 'extra-5-areal-cuda-vllm' and extra == 'extra-5-areal-sglang') or (sys_platform == 'linux' and extra == 'extra-5-areal-sglang' and extra == 'extra-5-areal-vllm')" }, - { name = "opentelemetry-exporter-prometheus", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (sys_platform != 'darwin' and sys_platform != 'linux' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (sys_platform 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"(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "colorful", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "grpcio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "opencensus", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "opentelemetry-exporter-prometheus", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "opentelemetry-proto", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "opentelemetry-sdk", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "prometheus-client", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "py-spy", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pydantic", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "requests", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "smart-open", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "virtualenv", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, ] [[package]] @@ -6186,7 +5032,7 @@ name = "redis" version = "7.3.0" source = { registry = "https://pypi.org/simple" } dependencies = [ - { name = "async-timeout", marker = "(python_full_version < '3.11.3' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.11.3' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.11.3' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.11.3' and platform_machine == 'x86_64' and sys_platform == 'linux') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda' and extra == 'extra-5-areal-vllm') or (platform_machine != 'arm64' and platform_machine != 'x86_64' and sys_platform == 'darwin' and extra == 'extra-5-areal-cuda-sglang' and extra == 'extra-5-areal-cuda-vllm') or (platform_machine != 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'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "orjson", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "packaging", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pandas", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pebble", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "peft", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pillow", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "prettytable", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "psutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pybase64", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pybind11", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pydantic", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pylatexenc", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "python-dateutil", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "python-debian", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "python-dotenv", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pyyaml", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "pyzmq", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "qwen-agent", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ray", version = "2.49.2", source = { registry = "https://pypi.org/simple" }, extra = ["default"], marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "ray", version = "2.54.0", source = { registry = "https://pypi.org/simple" }, extra = ["default"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "redis", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "regex", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "rich", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "seaborn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "sentencepiece", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "setproctitle", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "swanboard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "swanlab", extra = ["dashboard"], marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tabulate", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tenacity", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "tensorboardx", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "timeout-decorator", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torch", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "torch", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchao", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.2.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "torchaudio", version = "2.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchdata", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchvision", version = "0.17.2", source = { registry = "https://pypi.org/simple" }, marker = "platform_machine == 'x86_64' and sys_platform == 'darwin'" }, + { name = "torchvision", version = "0.25.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux')" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "tqdm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "trackio", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "transformers", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvicorn", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "uvloop", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wandb", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "wheel", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "word2number", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "zstandard", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] + +[package.optional-dependencies] +cuda = [ + { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +cuda-train = [ + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] +flash-attn = [ + { name = "flash-attn", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +kernels = [ + { name = "kernels", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] +megatron = [ + { name = "mbridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-bridge", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "megatron-core", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] +tms = [ + { name = "torch-memory-saver", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, +] +vllm = [ + { name = "torch", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchaudio", version = "2.10.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "torchvision", version = "0.25.0+cu129", source = { registry = "https://download.pytorch.org/whl/cu129" }, marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, + { name = "vllm", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" }, +] + +[package.dev-dependencies] +dev = [ + { name = "clang-format", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ipython", version = "9.10.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "ipython", version = "9.11.0", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.12' and platform_machine == 'arm64' and sys_platform == 'darwin') or (python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'darwin') or (python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "jupyter-book", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat-frontmatter", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat-gfm", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "mdformat-tables", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine == 'x86_64' and sys_platform == 'linux')" }, + { name = "plotly", marker = "(platform_machine == 'arm64' and sys_platform == 'darwin') or (platform_machine == 'x86_64' and sys_platform == 'darwin') or (platform_machine == 'aarch64' and sys_platform == 'linux') or 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