From 1dfc42d58e488f035af96c5b7ea97d273732355f Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 26 Jun 2024 21:42:01 +0000 Subject: [PATCH 001/114] added --- run-example.py | 5 + vllm/model_executor/layers/linear.py | 2 +- .../compressed_tensors/compressed_tensors.py | 14 +- .../compressed_tensors/schemes/__init__.py | 1 + .../schemes/compressed_tensors_w8a8_fp8.py | 133 ++++++++++++++++++ 5 files changed, 152 insertions(+), 3 deletions(-) create mode 100644 run-example.py create mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py diff --git a/run-example.py b/run-example.py new file mode 100644 index 0000000000000..a44b9b8452f50 --- /dev/null +++ b/run-example.py @@ -0,0 +1,5 @@ +from vllm import LLM + +model = LLM("nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test") +breakpoint() +print(model.generate("Hello my name is")) \ No newline at end of file diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py index 45f805547b414..4ce2b5179dde5 100644 --- a/vllm/model_executor/layers/linear.py +++ b/vllm/model_executor/layers/linear.py @@ -772,7 +772,7 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): if fp8_scales_shard_indexer is None and len(loaded_weight.shape) == 0: loaded_weight = loaded_weight.reshape(1) - + # breakpoint() assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index c69e2f3bcf9fa..eb7404a64da17 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -2,18 +2,19 @@ import torch from pydantic import BaseModel +from torch.nn import Module from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase from vllm.model_executor.layers.quantization.base_config import ( # noqa: E501 QuantizationConfig) from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( - W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, + W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensorsFp8, CompressedTensorsScheme, CompressedTensorsW4A16Sparse24, CompressedTensorsW8A8DynamicToken, CompressedTensorsW8A8StaticTensor, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( CompressionFormat, QuantizationArgs, QuantizationStrategy, - find_first_name_or_class_match) + QuantizationType, find_first_name_or_class_match) class CompressedTensorsConfig(QuantizationConfig): @@ -121,6 +122,9 @@ def _is_wNa16_group_channel(self, weight_quant: BaseModel, return (is_channel_group and input_quant_none and is_symmetric and is_static) + def _is_fp8(self, weight_quant: BaseModel, input_quant: BaseModel) -> bool: + return weight_quant.type == QuantizationType.FLOAT + def _get_schema(self, weight_quant: BaseModel, input_quant: BaseModel) -> "CompressedTensorsScheme": @@ -139,6 +143,9 @@ def _get_schema(self, weight_quant: BaseModel, group_size=weight_quant.group_size) if self.quant_format == CompressionFormat.int_quantized.value: + if self._is_fp8(weight_quant, input_quant): + return CompressedTensorsFp8() + if self._is_static_tensor_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8StaticTensor( strategy=weight_quant.strategy) @@ -177,6 +184,9 @@ class CompressedTensorsLinearMethod(LinearMethodBase): def __init__(self, quantization_config: CompressedTensorsConfig): self.quantization_config = quantization_config + def process_weights_after_loading(self, layer: Module) -> None: + layer.scheme.process_weights_after_loading(layer) + def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, output_partition_sizes: List[int], input_size: int, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index f6d20ce2c6f77..271482847d260 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -5,6 +5,7 @@ W4A16SPARSE24_SUPPORTED_BITS, CompressedTensorsW4A16Sparse24) from .compressed_tensors_w8a8_dynamictoken import ( # noqa: F401, E501 CompressedTensorsW8A8DynamicToken) +from .compressed_tensors_w8a8_fp8 import CompressedTensorsFp8 # noqa: F401 from .compressed_tensors_w8a8_statictensor import ( # noqa: F401, E501 CompressedTensorsW8A8StaticTensor) from .compressed_tensors_wNa16 import WNA16_SUPPORTED_BITS # noqa: F401 diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py new file mode 100644 index 0000000000000..98afe52cd2c46 --- /dev/null +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -0,0 +1,133 @@ +from typing import Callable, List, Optional, Tuple, Union + +import torch +from torch.nn import Parameter, Module + +from vllm import _custom_ops as ops +from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( + CompressedTensorsScheme) +from vllm.model_executor.utils import set_weight_attrs + +__all__ = ["CompressedTensorsW8A8FP8"] + + +class CompressedTensorsFp8(CompressedTensorsScheme): + def create_weights(self, + layer: torch.nn.Module, + output_partition_sizes: List[int], + input_size_per_partition: int, + params_dtype: torch.dtype, + weight_loader: Callable, + **kwargs): + output_size_per_partition = sum(output_partition_sizes) + layer.logical_widths = output_partition_sizes + + weight = Parameter(torch.empty(output_size_per_partition, + input_size_per_partition, + dtype=torch.float8_e4m3fn), + requires_grad=False) + layer.register_parameter("weight", weight) + set_weight_attrs(weight, { + "input_dim": 1, + "output_dim": 0, + "weight_loader": weight_loader, + }) + + weight_scale = Parameter(torch.empty(len(output_partition_sizes), + dtype=torch.float32), + requires_grad=False) + + layer.register_parameter("weight_scale", weight_scale) + set_weight_attrs(weight_scale, { + "weight_loader": weight_loader, + "ignore_warning": True, + "fp8_scales_shard_indexer": self.scales_shard_indexer, + }) + + input_scale = Parameter(torch.empty(len(output_partition_sizes), + dtype=torch.float32), + requires_grad=False) + + layer.register_parameter("input_scale", input_scale) + set_weight_attrs(input_scale, { + "weight_loader": weight_loader, + "ignore_warning": True, + "fp8_scales_shard_indexer": self.scales_shard_indexer, + }) + + + def scales_shard_indexer(self, + param: torch.Tensor, + loaded_weight: torch.Tensor, + shard_id: Union[str, int]) -> Tuple[torch.Tensor, torch.Tensor]: + + qkv_idxs = {"q": 0, "k": 1, "v": 2} + + if isinstance(shard_id, int): + pass + elif isinstance(shard_id, str): + if shard_id not in qkv_idxs: + raise ValueError(f"Unknown shard_id: {shard_id}") + shard_id = qkv_idxs[shard_id] + else: + ValueError(f"Shard id must be int or str but got {type(shard_id)}") + + return param[shard_id].reshape(-1), loaded_weight + + + def process_weights_after_loading(self, layer) -> None: + # WEIGHT_SCALE / WEIGHT + # Loop over logical weights, requantizing with single scale. + max_w_scale = layer.weight_scale.max() + start = 0 + for idx, logical_width in enumerate(layer.logical_widths): + end = start + logical_width + weight_dq = per_tensor_dequantize(layer.weight[start:end, :], + layer.weight_scale[idx]) + + layer.weight[start:end, :] = per_tensor_quantize( + weight_dq, layer.weight_scale.max()) + start = end + layer.weight_scale = Parameter(max_w_scale, requires_grad=False) + + # WEIGHT + # Transpose weight for passing to torch._scaled_mm + weight = layer.weight + layer.weight = Parameter(weight.t(), requires_grad=False) + layer.input_scale = Parameter(layer.input_scale.max(), + requires_grad=False) + + def apply_weights(self, + layer: torch.nn.Module, + x: torch.Tensor, + bias: Optional[torch.Tensor] = None) -> torch.Tensor: + + assert bias is None + + qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale) + + # Fused GEMM_DQ + output = ops.cutlass_scaled_mm( + qinput, + layer.weight, + out_dtype=x.dtype, + scale_a=x_scale, + scale_b=layer.weight_scale, + ) + + return torch.narrow(output, 0, 0, x.shape[0]) + + +def per_tensor_quantize(tensor: torch.Tensor, + inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: + finfo = torch.finfo(torch.float8_e4m3fn) + qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) + return qweight.to(torch.float8_e4m3fn) + + +def per_tensor_dequantize( + tensor: torch.Tensor, inv_scale: Union[float, + torch.Tensor]) -> torch.Tensor: + fake_qweight = tensor.to(torch.float16) + dq_weight = fake_qweight * inv_scale + return dq_weight From aa4a9f54b9b4246bbb834d560e94e833e61e6840 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 26 Jun 2024 21:44:45 +0000 Subject: [PATCH 002/114] nits --- run-example.py | 5 ----- vllm/model_executor/layers/linear.py | 1 - 2 files changed, 6 deletions(-) delete mode 100644 run-example.py diff --git a/run-example.py b/run-example.py deleted file mode 100644 index a44b9b8452f50..0000000000000 --- a/run-example.py +++ /dev/null @@ -1,5 +0,0 @@ -from vllm import LLM - -model = LLM("nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test") -breakpoint() -print(model.generate("Hello my name is")) \ No newline at end of file diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py index 4ce2b5179dde5..9cc9b637418d2 100644 --- a/vllm/model_executor/layers/linear.py +++ b/vllm/model_executor/layers/linear.py @@ -772,7 +772,6 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): if fp8_scales_shard_indexer is None and len(loaded_weight.shape) == 0: loaded_weight = loaded_weight.reshape(1) - # breakpoint() assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) From 27f9a03ffe31f898ea78f49b2f1c3ced08dba31b Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 26 Jun 2024 21:49:11 +0000 Subject: [PATCH 003/114] cleanup --- .../quantization/compressed_tensors/compressed_tensors.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index eb7404a64da17..de03a567f0ff3 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -2,7 +2,6 @@ import torch from pydantic import BaseModel -from torch.nn import Module from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase from vllm.model_executor.layers.quantization.base_config import ( # noqa: E501 @@ -184,8 +183,9 @@ class CompressedTensorsLinearMethod(LinearMethodBase): def __init__(self, quantization_config: CompressedTensorsConfig): self.quantization_config = quantization_config - def process_weights_after_loading(self, layer: Module) -> None: - layer.scheme.process_weights_after_loading(layer) + def process_weights_after_loading(self, layer: torch.nn.Module) -> None: + if hasattr(layer.scheme, "process_weights" and layer.scheme.process_weights): + layer.scheme.process_weights_after_loading(layer) def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, From de7a0649c89c55a58fe7a1bbdbaffb68160010ce Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Thu, 27 Jun 2024 02:43:15 +0000 Subject: [PATCH 004/114] stash --- .../quantization/compressed_tensors/compressed_tensors.py | 2 +- .../compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index de03a567f0ff3..f140542fa1726 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -184,7 +184,7 @@ def __init__(self, quantization_config: CompressedTensorsConfig): self.quantization_config = quantization_config def process_weights_after_loading(self, layer: torch.nn.Module) -> None: - if hasattr(layer.scheme, "process_weights" and layer.scheme.process_weights): + if hasattr(layer.scheme, "process_weights") and layer.scheme.process_weights: layer.scheme.process_weights_after_loading(layer) def create_weights(self, layer: torch.nn.Module, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index 98afe52cd2c46..918f84efd846b 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -19,6 +19,8 @@ def create_weights(self, params_dtype: torch.dtype, weight_loader: Callable, **kwargs): + + self.process_weights=True output_size_per_partition = sum(output_partition_sizes) layer.logical_widths = output_partition_sizes From ec6a833d05f1ed943e2088b895f3c5ef5e6c7c09 Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:02:23 +0000 Subject: [PATCH 005/114] refactor gptq marlin --- benchmarks/kernels/benchmark_marlin.py | 4 +- tests/kernels/test_marlin_gemm.py | 4 +- .../schemes/compressed_tensors_wNa16.py | 69 +++-- .../layers/quantization/gptq_marlin.py | 246 +++++------------- .../quantization/test_utils/__init__.py | 0 .../{utils => test_utils}/format_24.py | 0 .../{utils => test_utils}/marlin_24_perms.py | 0 .../{utils => test_utils}/marlin_perms.py | 0 .../{utils => test_utils}/marlin_utils.py | 0 .../layers/quantization/utils/marlin.py | 129 +++++++++ 10 files changed, 256 insertions(+), 196 deletions(-) create mode 100644 vllm/model_executor/layers/quantization/test_utils/__init__.py rename vllm/model_executor/layers/quantization/{utils => test_utils}/format_24.py (100%) rename vllm/model_executor/layers/quantization/{utils => test_utils}/marlin_24_perms.py (100%) rename vllm/model_executor/layers/quantization/{utils => test_utils}/marlin_perms.py (100%) rename vllm/model_executor/layers/quantization/{utils => test_utils}/marlin_utils.py (100%) create mode 100644 vllm/model_executor/layers/quantization/utils/marlin.py diff --git a/benchmarks/kernels/benchmark_marlin.py b/benchmarks/kernels/benchmark_marlin.py index 261f5829631ee..e549ebfb2f80e 100644 --- a/benchmarks/kernels/benchmark_marlin.py +++ b/benchmarks/kernels/benchmark_marlin.py @@ -11,9 +11,9 @@ from vllm.model_executor.layers.quantization.gptq_marlin_24 import ( GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS) -from vllm.model_executor.layers.quantization.utils.marlin_utils import ( +from vllm.model_executor.layers.quantization.test_utils.marlin_utils import ( MarlinWorkspace, marlin_24_quantize, marlin_quantize) -from vllm.model_executor.layers.quantization.utils.quant_utils import ( +from vllm.model_executor.layers.quantization.test_utils.quant_utils import ( gptq_pack, quantize_weights, sort_weights) from vllm.utils import FlexibleArgumentParser diff --git a/tests/kernels/test_marlin_gemm.py b/tests/kernels/test_marlin_gemm.py index 1f8d94bad26d9..05acd626b45eb 100644 --- a/tests/kernels/test_marlin_gemm.py +++ b/tests/kernels/test_marlin_gemm.py @@ -12,9 +12,9 @@ from vllm.model_executor.layers.quantization.gptq_marlin_24 import ( GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS) -from vllm.model_executor.layers.quantization.utils.marlin_perms import ( +from vllm.model_executor.layers.quantization.test_utils.marlin_perms import ( marlin_perm) -from vllm.model_executor.layers.quantization.utils.marlin_utils import ( +from vllm.model_executor.layers.quantization.test_utils.marlin_utils import ( MarlinWorkspace, compute_max_diff, is_marlin_supported, marlin_24_quantize, marlin_quantize, marlin_weights) from vllm.model_executor.layers.quantization.utils.quant_utils import ( diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py index 2243260053ef5..856d97d790567 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py @@ -6,7 +6,7 @@ from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) -from vllm.model_executor.layers.quantization.gptq_marlin import ( +from vllm.model_executor.layers.quantization.utils.marlin import ( GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, GPTQMarlinState, marlin_permute_scales) from vllm.model_executor.utils import set_weight_attrs @@ -20,10 +20,12 @@ class CompressedTensorsWNA16(CompressedTensorsScheme): def __init__(self, strategy: str, num_bits: int, - group_size: Optional[int] = None): + group_size: Optional[int] = None, + act_order: bool = False): self.num_bits = num_bits self.strategy = strategy self.group_size = group_size + self.act_order = act_order if self.strategy == "group" and self.group_size is None: raise ValueError( @@ -73,6 +75,23 @@ def create_weights(self, layer: torch.nn.Module, input_size: int, }) layer.register_parameter("weight_packed", weight) + # Activation order + g_idx = Parameter( + torch.empty( + input_size_per_partition, + dtype=torch.int32, + ), + requires_grad=False, + ) + set_weight_attrs( + g_idx, { + "input_dim": 0, + "weight_loader": weight_loader, + "ignore_warning": True + }, + ) + layer.register_parameter("weight_g_idx", g_idx) + weight_scale = Parameter( torch.empty( output_size_per_partition, @@ -107,6 +126,9 @@ def create_weights(self, layer: torch.nn.Module, input_size: int, layer.input_size = input_size layer.marlin_state = GPTQMarlinState.REPACK layer.is_k_full = True + if self.act_order: + assert self.quant_config.group_size != -1 + layer.is_k_full = input_size_per_partition == input_size layer.group_size = group_size max_workspace_size = ( @@ -124,6 +146,7 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): size_m = reshaped_x.shape[0] part_size_n = layer.output_size_per_partition part_size_k = layer.input_size_per_partition + full_size_k = layer.input_size out_shape = x.shape[:-1] + (part_size_n, ) @@ -141,33 +164,49 @@ def replace_tensor(name, new_t): cur_device = layer.weight_packed.device - # Reset g_idx related tensors - layer.g_idx = Parameter(torch.empty(0, - dtype=torch.int, - device=cur_device), - requires_grad=False) - layer.g_idx_sort_indices = Parameter(torch.empty( - 0, dtype=torch.int, device=cur_device), - requires_grad=False) + # Process act_order + if self.act_order: + g_idx_sort_indices = Parameter( + torch.argsort(layer.weight_g_idx).to(torch.int), + requires_grad=False) + + sorted_g_idx = layer.g_idx[g_idx_sort_indices] + replace_tensor("weight_g_idx", sorted_g_idx) + + else: + layer.weight_g_idx = Parameter( + torch.empty(0, dtype=torch.int, device=cur_device), + requires_grad=False) + layer.g_idx_sort_indices = Parameter( + torch.empty(0, dtype=torch.int, device=cur_device), + requires_grad=False) # Repack weights marlin_qweight = ops.gptq_marlin_repack( - layer.weight_packed.t().contiguous(), layer.g_idx_sort_indices, - part_size_k, part_size_n, self.num_bits) + layer.weight_packed.t().contiguous(), + layer.g_idx_sort_indices, + part_size_k, + part_size_n, + self.num_bits) replace_tensor("weight_packed", marlin_qweight) # Permute scales scales_size_k = part_size_k scales_size_n = part_size_n + if self.act_order: + scales_size_k = full_size_k marlin_scales = marlin_permute_scales( - layer.weight_scale.squeeze().t().contiguous(), scales_size_k, - scales_size_n, layer.group_size, self.num_bits) + layer.weight_scale.squeeze().t().contiguous(), + scales_size_k, + scales_size_n, + self.group_size, + self.num_bits) replace_tensor("weight_scale", marlin_scales) output = ops.gptq_marlin_gemm(reshaped_x, layer.weight_packed, - layer.weight_scale, layer.g_idx, + layer.weight_scale, layer.weight_g_idx, layer.g_idx_sort_indices, layer.workspace, self.num_bits, size_m, part_size_n, part_size_k, diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 97aae33f133be..628fce2a7d9f5 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -1,8 +1,7 @@ -import enum -from enum import Enum from typing import Any, Dict, List, Optional import torch +from torch.nn.modules import Module from torch.nn.parameter import Parameter from vllm import _custom_ops as ops @@ -11,50 +10,13 @@ set_weight_attrs) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) +from vllm.model_executor.layers.quantization.utils.marlin import ( + check_marlin_supported, verify_marlin_supported, verify_marlin_supports_shape, + marlin_permute_scales, get_max_workspace_size, sort_g_idx, replace_tensor) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead -from vllm.utils import get_device_capability_stateless - -logger = init_logger(__name__) - -GPTQ_MARLIN_TILE = 16 -GPTQ_MARLIN_MIN_THREAD_N = 64 -GPTQ_MARLIN_MIN_THREAD_K = 128 -GPTQ_MARLIN_MAX_PARALLEL = 16 - -GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8] -GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128] -GPTQ_MARLIN_SUPPORTED_SYM = [True] - - -# Permutations for Marlin scale shuffling -def get_scale_perms(num_bits: int): - scale_perm: List[int] = [] - for i in range(8): - scale_perm.extend([i + 8 * j for j in range(8)]) - scale_perm_single: List[int] = [] - for i in range(4): - scale_perm_single.extend( - [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) - return scale_perm, scale_perm_single -def get_pack_factor(num_bits: int): - assert (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS - ), f"Unsupported num_bits = {num_bits}" - return 32 // num_bits - - -def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, - group_size: int, num_bits: int): - scale_perm, scale_perm_single = get_scale_perms(num_bits) - if group_size < size_k and group_size != -1: - s = s.reshape((-1, len(scale_perm)))[:, scale_perm] - else: - s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single] - s = s.reshape((-1, size_n)).contiguous() - - return s - +logger = init_logger(__name__) class GPTQMarlinConfig(QuantizationConfig): """Config class for GPTQ Marlin""" @@ -67,33 +29,16 @@ def __init__(self, weight_bits: int, group_size: int, desc_act: bool, desc_act = False self.weight_bits = weight_bits + self.pack_factor = 32 // self.weight_bits # packed into int32 self.group_size = group_size self.desc_act = desc_act self.is_sym = is_sym self.lm_head_quantized = lm_head_quantized - # Verify - if self.weight_bits not in GPTQ_MARLIN_SUPPORTED_NUM_BITS: - raise ValueError( - f"Marlin does not support weight_bits = {self.weight_bits}. " - f"Only weight_bits = {GPTQ_MARLIN_SUPPORTED_NUM_BITS} " - "are supported.") - if self.group_size not in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES: - raise ValueError( - f"Marlin does not support group_size = {self.group_size}. " - f"Only group_sizes = {GPTQ_MARLIN_SUPPORTED_GROUP_SIZES} " - "are supported.") - if self.is_sym not in GPTQ_MARLIN_SUPPORTED_SYM: - raise ValueError( - f"Marlin does not support is_sym = {self.is_sym}. " - f"Only sym = {GPTQ_MARLIN_SUPPORTED_SYM} are supported.") - - # Init - self.pack_factor = get_pack_factor(weight_bits) - self.tile_size = GPTQ_MARLIN_TILE - self.min_thread_n = GPTQ_MARLIN_MIN_THREAD_N - self.min_thread_k = GPTQ_MARLIN_MIN_THREAD_K - self.max_parallel = GPTQ_MARLIN_MAX_PARALLEL + verify_marlin_supported( + num_bits=self.weight_bits, + group_size=self.group_size, + is_sym=self.is_sym) def __repr__(self) -> str: return (f"GPTQMarlinConfig(weight_bits={self.weight_bits}, " @@ -172,21 +117,12 @@ def is_marlin_compatible(cls, quant_config: Dict[str, Any]): or desc_act is None): return False - # If the capability of the device is too low, cannot convert. - major, minor = get_device_capability_stateless() - device_capability = major * 10 + minor - if device_capability < cls.get_min_capability(): - return False - - # Otherwise, can convert if model satisfies marlin constraints. - return (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS - and group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES - and sym in GPTQ_MARLIN_SUPPORTED_SYM) - - -class GPTQMarlinState(Enum): - REPACK = enum.auto() - READY = enum.auto() + return check_marlin_supported( + num_bits=num_bits, + group_size=group_size, + is_sym=sym, + min_capability=cls.get_min_capability() + ) class GPTQMarlinLinearMethod(LinearMethodBase): @@ -211,37 +147,20 @@ def create_weights( ) -> None: del output_size + output_size_per_partition = sum(output_partition_sizes) + # Normalize group_size if self.quant_config.group_size != -1: group_size = self.quant_config.group_size else: group_size = input_size - - # Validate dtype - if params_dtype not in [torch.float16, torch.bfloat16]: - raise ValueError(f"The params dtype must be float16 " - f"or bfloat16, but got {params_dtype}") - - # Validate output_size_per_partition - output_size_per_partition = sum(output_partition_sizes) - if output_size_per_partition % self.quant_config.min_thread_n != 0: - raise ValueError( - f"Weight output_size_per_partition = " - f"{output_size_per_partition} is not divisible by " - f" min_thread_n = {self.quant_config.min_thread_n}.") - - # Validate input_size_per_partition - if input_size_per_partition % self.quant_config.min_thread_k != 0: - raise ValueError( - f"Weight input_size_per_partition = " - f"{input_size_per_partition} is not divisible " - f"by min_thread_k = {self.quant_config.min_thread_k}.") - - if (group_size < input_size - and input_size_per_partition % group_size != 0): - raise ValueError( - f"Weight input_size_per_partition = {input_size_per_partition}" - f" is not divisible by group_size = {group_size}.") + + verify_marlin_supports_shape( + output_size_per_partition=sum(output_partition_sizes), + input_size_per_partition=input_size_per_partition, + input_size=input_size, + group_size=group_size + ) # Detect sharding of scales/zp @@ -252,7 +171,6 @@ def create_weights( if self.quant_config.desc_act: # Act-order case assert self.quant_config.group_size != -1 - is_k_full = input_size_per_partition == input_size else: @@ -293,7 +211,7 @@ def create_weights( # Activation order g_idx = Parameter( torch.empty( - input_size_per_partition, + input_size_per_partition if self.quant_config.desc_act else 0, dtype=torch.int32, ), requires_grad=False, @@ -352,12 +270,10 @@ def create_weights( ) # Allocate marlin workspace - max_workspace_size = ( - output_size_per_partition // - self.quant_config.min_thread_n) * self.quant_config.max_parallel - workspace = torch.zeros(max_workspace_size, - dtype=torch.int, - requires_grad=False) + workspace = torch.zeros( + get_max_workspace_size(output_size_per_partition), + dtype=torch.int, + requires_grad=False) layer.register_parameter("qweight", qweight) layer.register_parameter("g_idx", g_idx) @@ -369,7 +285,44 @@ def create_weights( layer.output_size_per_partition = output_size_per_partition layer.input_size = input_size layer.is_k_full = is_k_full - layer.marlin_state = GPTQMarlinState.REPACK + + + def process_weights_after_loading(self, layer: Module) -> None: + + # To be used as part of repacking + part_size_n = layer.output_size_per_partition + part_size_k = layer.input_size_per_partition + full_size_k = layer.input_size + + # Sort for act_order + if self.quant_config.desc_act: + # Get sorting based on g_idx + sort_g_idx(layer, "g_idx", "g_idx_sort_indices") + + # Repack weights into marlin format + marlin_qweight = ops.gptq_marlin_repack( + layer.qweight, + layer.g_idx_sort_indices, + part_size_k, + part_size_n, + self.quant_config.weight_bits, + ) + replace_tensor(layer, "qweight", marlin_qweight) + + # Permute scales + scales_size_k = part_size_k + scales_size_n = part_size_n + if self.quant_config.desc_act: + scales_size_k = full_size_k + + marlin_scales = marlin_permute_scales( + layer.scales, + scales_size_k, + scales_size_n, + self.quant_config.group_size, + self.quant_config.weight_bits, + ) + replace_tensor(layer, "scales", marlin_scales) def apply( self, @@ -382,70 +335,9 @@ def apply( size_m = reshaped_x.shape[0] part_size_n = layer.output_size_per_partition part_size_k = layer.input_size_per_partition - full_size_k = layer.input_size out_shape = x.shape[:-1] + (part_size_n, ) - - if layer.marlin_state == GPTQMarlinState.REPACK: - layer.marlin_state = GPTQMarlinState.READY - - # Newly generated tensors need to replace existing tensors that are - # already registered as parameters by vLLM (and won't be freed) - def replace_tensor(name, new_t): - # It is important to use resize_() here since it ensures - # the same buffer is reused - getattr(layer, name).resize_(new_t.shape) - getattr(layer, name).copy_(new_t) - del new_t - - cur_device = layer.qweight.device - - # Process act_order - if self.quant_config.desc_act: - # Get sorting based on g_idx - g_idx_sort_indices = torch.argsort(layer.g_idx).to(torch.int) - - sorted_g_idx = layer.g_idx[g_idx_sort_indices] - - replace_tensor("g_idx", sorted_g_idx) - replace_tensor("g_idx_sort_indices", g_idx_sort_indices) - - else: - # Reset g_idx related tensors - layer.g_idx = Parameter( - torch.empty(0, dtype=torch.int, device=cur_device), - requires_grad=False, - ) - layer.g_idx_sort_indices = Parameter( - torch.empty(0, dtype=torch.int, device=cur_device), - requires_grad=False, - ) - - # Repack weights - marlin_qweight = ops.gptq_marlin_repack( - layer.qweight, - layer.g_idx_sort_indices, - part_size_k, - part_size_n, - self.quant_config.weight_bits, - ) - replace_tensor("qweight", marlin_qweight) - - # Permute scales - scales_size_k = part_size_k - scales_size_n = part_size_n - if self.quant_config.desc_act: - scales_size_k = full_size_k - - marlin_scales = marlin_permute_scales( - layer.scales, - scales_size_k, - scales_size_n, - self.quant_config.group_size, - self.quant_config.weight_bits, - ) - replace_tensor("scales", marlin_scales) - + output = ops.gptq_marlin_gemm( reshaped_x, layer.qweight, diff --git a/vllm/model_executor/layers/quantization/test_utils/__init__.py b/vllm/model_executor/layers/quantization/test_utils/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/vllm/model_executor/layers/quantization/utils/format_24.py b/vllm/model_executor/layers/quantization/test_utils/format_24.py similarity index 100% rename from vllm/model_executor/layers/quantization/utils/format_24.py rename to vllm/model_executor/layers/quantization/test_utils/format_24.py diff --git a/vllm/model_executor/layers/quantization/utils/marlin_24_perms.py b/vllm/model_executor/layers/quantization/test_utils/marlin_24_perms.py similarity index 100% rename from vllm/model_executor/layers/quantization/utils/marlin_24_perms.py rename to vllm/model_executor/layers/quantization/test_utils/marlin_24_perms.py diff --git a/vllm/model_executor/layers/quantization/utils/marlin_perms.py b/vllm/model_executor/layers/quantization/test_utils/marlin_perms.py similarity index 100% rename from vllm/model_executor/layers/quantization/utils/marlin_perms.py rename to vllm/model_executor/layers/quantization/test_utils/marlin_perms.py diff --git a/vllm/model_executor/layers/quantization/utils/marlin_utils.py b/vllm/model_executor/layers/quantization/test_utils/marlin_utils.py similarity index 100% rename from vllm/model_executor/layers/quantization/utils/marlin_utils.py rename to vllm/model_executor/layers/quantization/test_utils/marlin_utils.py diff --git a/vllm/model_executor/layers/quantization/utils/marlin.py b/vllm/model_executor/layers/quantization/utils/marlin.py new file mode 100644 index 0000000000000..0d1472f8279b1 --- /dev/null +++ b/vllm/model_executor/layers/quantization/utils/marlin.py @@ -0,0 +1,129 @@ +import enum +from typing import List + +import torch + +from vllm.utils import get_device_capability_stateless + +class GPTQMarlinState(enum.Enum): + REPACK = enum.auto() + READY = enum.auto() + +GPTQ_MARLIN_TILE = 16 +GPTQ_MARLIN_MIN_THREAD_N = 64 +GPTQ_MARLIN_MIN_THREAD_K = 128 +GPTQ_MARLIN_MAX_PARALLEL = 16 + +GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8] +GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128] +GPTQ_MARLIN_SUPPORTED_SYM = [True] +GTPQ_MARLIN_UNSUPPORTED_GROUP_SIZE_ACT_ORDER = [-1] + +# Permutations for Marlin scale shuffling +def get_scale_perms(num_bits: int): + scale_perm: List[int] = [] + for i in range(8): + scale_perm.extend([i + 8 * j for j in range(8)]) + scale_perm_single: List[int] = [] + for i in range(4): + scale_perm_single.extend( + [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) + return scale_perm, scale_perm_single + + +def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, + group_size: int, num_bits: int): + scale_perm, scale_perm_single = get_scale_perms(num_bits) + if group_size < size_k and group_size != -1: + s = s.reshape((-1, len(scale_perm)))[:, scale_perm] + else: + s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single] + s = s.reshape((-1, size_n)).contiguous() + + return s + +def check_marlin_supported(num_bits: int, group_size: int, + is_sym: bool, min_capability: int) -> bool: + + # If the capability of the device is too low, cannot convert. + major, minor = get_device_capability_stateless() + device_capability = major * 10 + minor + if device_capability < min_capability: + return False + + return (device_capability >= min_capability + and num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS + and group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES + and is_sym in GPTQ_MARLIN_SUPPORTED_SYM) + + +def verify_marlin_supported(num_bits: int, group_size: int, + is_sym: bool) -> None: + + if num_bits not in GPTQ_MARLIN_SUPPORTED_NUM_BITS: + raise ValueError( + f"Marlin does not support weight_bits = {num_bits}. " + f"Only weight_bits = {GPTQ_MARLIN_SUPPORTED_NUM_BITS} " + "are supported.") + if group_size not in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES: + raise ValueError( + f"Marlin does not support group_size = {group_size}. " + f"Only group_sizes = {GPTQ_MARLIN_SUPPORTED_GROUP_SIZES} " + "are supported.") + if is_sym not in GPTQ_MARLIN_SUPPORTED_SYM: + raise ValueError( + f"Marlin does not support is_sym = is_sym. " + f"Only sym = {GPTQ_MARLIN_SUPPORTED_SYM} are supported.") + + +def verify_marlin_supports_shape(output_size_per_partition: int, + input_size_per_partition: int, + input_size: int, group_size: int) -> None: + + # Validate output_size_per_partition + if output_size_per_partition % GPTQ_MARLIN_MIN_THREAD_N != 0: + raise ValueError( + f"Weight output_size_per_partition = " + f"{output_size_per_partition} is not divisible by " + f" min_thread_n = {GPTQ_MARLIN_MIN_THREAD_N}. " + "Consider reducing tensor_parallel_size or running " + "with --quantization gptq.") + + # Validate input_size_per_partition + if input_size_per_partition % GPTQ_MARLIN_MIN_THREAD_K != 0: + raise ValueError( + f"Weight input_size_per_partition = " + f"{input_size_per_partition} is not divisible " + f"by min_thread_k = {GPTQ_MARLIN_MIN_THREAD_K}. " + "Consider reducing tensor_parallel_size or running " + "with --quantization gptq.") + + if (group_size < input_size + and input_size_per_partition % group_size != 0): + raise ValueError( + f"Weight input_size_per_partition = {input_size_per_partition}" + f" is not divisible by group_size = {group_size}." + "Consider reducing tensor_parallel_size or running " + "with --quantization gptq.") + +def get_max_workspace_size(output_size_per_partition: int): + return (output_size_per_partition // + GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL + +# Newly generated tensors need to replace existing tensors that are +# already registered as parameters by vLLM (and won't be freed) +def replace_tensor(layer: torch.nn.Module, name: str, new_t: torch.Tensor) -> None: + # It is important to use resize_() here since it ensures + # the same buffer is reused + getattr(layer, name).resize_(new_t.shape) + getattr(layer, name).copy_(new_t) + del new_t + +# Perform softing +def sort_g_idx(layer: torch.nn.Module, + g_idx_name: str, + g_idx_sort_indices_name: str): + g_idx = getattr(layer, g_idx_name) + g_idx_sort_indices = torch.argsort(g_idx).to(torch.int) + replace_tensor(layer, g_idx_name, g_idx[g_idx_sort_indices]) + replace_tensor(layer, g_idx_sort_indices_name, g_idx_sort_indices) From 966f7be00fd377fcc74bcbe833594ae8c07802bc Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:05:07 +0000 Subject: [PATCH 006/114] back out w4a16 act-order compressed tensors --- .../schemes/compressed_tensors_wNa16.py | 21 +------------------ 1 file changed, 1 insertion(+), 20 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py index 856d97d790567..fb12d234f18f5 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py @@ -20,12 +20,10 @@ class CompressedTensorsWNA16(CompressedTensorsScheme): def __init__(self, strategy: str, num_bits: int, - group_size: Optional[int] = None, - act_order: bool = False): + group_size: Optional[int] = None): self.num_bits = num_bits self.strategy = strategy self.group_size = group_size - self.act_order = act_order if self.strategy == "group" and self.group_size is None: raise ValueError( @@ -75,23 +73,6 @@ def create_weights(self, layer: torch.nn.Module, input_size: int, }) layer.register_parameter("weight_packed", weight) - # Activation order - g_idx = Parameter( - torch.empty( - input_size_per_partition, - dtype=torch.int32, - ), - requires_grad=False, - ) - set_weight_attrs( - g_idx, { - "input_dim": 0, - "weight_loader": weight_loader, - "ignore_warning": True - }, - ) - layer.register_parameter("weight_g_idx", g_idx) - weight_scale = Parameter( torch.empty( output_size_per_partition, From d391f44b9ee025075bc9cece8a800bb5507551e3 Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:06:13 +0000 Subject: [PATCH 007/114] back out w4a16 act-order compressed tensors --- .../schemes/compressed_tensors_wNa16.py | 40 ++++++------------- 1 file changed, 13 insertions(+), 27 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py index fb12d234f18f5..681400b382535 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py @@ -145,30 +145,19 @@ def replace_tensor(name, new_t): cur_device = layer.weight_packed.device - # Process act_order - if self.act_order: - g_idx_sort_indices = Parameter( - torch.argsort(layer.weight_g_idx).to(torch.int), - requires_grad=False) - - sorted_g_idx = layer.g_idx[g_idx_sort_indices] - replace_tensor("weight_g_idx", sorted_g_idx) - - else: - layer.weight_g_idx = Parameter( - torch.empty(0, dtype=torch.int, device=cur_device), - requires_grad=False) - layer.g_idx_sort_indices = Parameter( - torch.empty(0, dtype=torch.int, device=cur_device), - requires_grad=False) + # Reset g_idx related tensors + layer.g_idx = Parameter(torch.empty(0, + dtype=torch.int, + device=cur_device), + requires_grad=False) + layer.g_idx_sort_indices = Parameter(torch.empty( + 0, dtype=torch.int, device=cur_device), + requires_grad=False) # Repack weights marlin_qweight = ops.gptq_marlin_repack( - layer.weight_packed.t().contiguous(), - layer.g_idx_sort_indices, - part_size_k, - part_size_n, - self.num_bits) + layer.weight_packed.t().contiguous(), layer.g_idx_sort_indices, + part_size_k, part_size_n, self.num_bits) replace_tensor("weight_packed", marlin_qweight) @@ -179,15 +168,12 @@ def replace_tensor(name, new_t): scales_size_k = full_size_k marlin_scales = marlin_permute_scales( - layer.weight_scale.squeeze().t().contiguous(), - scales_size_k, - scales_size_n, - self.group_size, - self.num_bits) + layer.weight_scale.squeeze().t().contiguous(), scales_size_k, + scales_size_n, layer.group_size, self.num_bits) replace_tensor("weight_scale", marlin_scales) output = ops.gptq_marlin_gemm(reshaped_x, layer.weight_packed, - layer.weight_scale, layer.weight_g_idx, + layer.weight_scale, layer.g_idx, layer.g_idx_sort_indices, layer.workspace, self.num_bits, size_m, part_size_n, part_size_k, From db075c3ac23baf86d685170f316c064d1f10ad37 Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:06:53 +0000 Subject: [PATCH 008/114] missed --- .../compressed_tensors/schemes/compressed_tensors_wNa16.py | 5 ----- 1 file changed, 5 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py index 681400b382535..ffa6f4005c028 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py @@ -107,9 +107,6 @@ def create_weights(self, layer: torch.nn.Module, input_size: int, layer.input_size = input_size layer.marlin_state = GPTQMarlinState.REPACK layer.is_k_full = True - if self.act_order: - assert self.quant_config.group_size != -1 - layer.is_k_full = input_size_per_partition == input_size layer.group_size = group_size max_workspace_size = ( @@ -164,8 +161,6 @@ def replace_tensor(name, new_t): # Permute scales scales_size_k = part_size_k scales_size_n = part_size_n - if self.act_order: - scales_size_k = full_size_k marlin_scales = marlin_permute_scales( layer.weight_scale.squeeze().t().contiguous(), scales_size_k, From 695dc0518a3c2389e32909ae04892455533ff287 Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:08:41 +0000 Subject: [PATCH 009/114] formatted' --- .../schemes/compressed_tensors_wNa16.py | 1 - .../layers/quantization/gptq_marlin.py | 38 +++++++-------- .../layers/quantization/utils/marlin.py | 47 ++++++++++--------- 3 files changed, 43 insertions(+), 43 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py index ffa6f4005c028..76fb9fc14ce90 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py @@ -124,7 +124,6 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): size_m = reshaped_x.shape[0] part_size_n = layer.output_size_per_partition part_size_k = layer.input_size_per_partition - full_size_k = layer.input_size out_shape = x.shape[:-1] + (part_size_n, ) diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 628fce2a7d9f5..40721ec55a745 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -11,13 +11,14 @@ from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) from vllm.model_executor.layers.quantization.utils.marlin import ( - check_marlin_supported, verify_marlin_supported, verify_marlin_supports_shape, - marlin_permute_scales, get_max_workspace_size, sort_g_idx, replace_tensor) + check_marlin_supported, get_max_workspace_size, marlin_permute_scales, + replace_tensor, sort_g_idx, verify_marlin_supported, + verify_marlin_supports_shape) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead - logger = init_logger(__name__) + class GPTQMarlinConfig(QuantizationConfig): """Config class for GPTQ Marlin""" @@ -29,16 +30,15 @@ def __init__(self, weight_bits: int, group_size: int, desc_act: bool, desc_act = False self.weight_bits = weight_bits - self.pack_factor = 32 // self.weight_bits # packed into int32 + self.pack_factor = 32 // self.weight_bits # packed into int32 self.group_size = group_size self.desc_act = desc_act self.is_sym = is_sym self.lm_head_quantized = lm_head_quantized - verify_marlin_supported( - num_bits=self.weight_bits, - group_size=self.group_size, - is_sym=self.is_sym) + verify_marlin_supported(num_bits=self.weight_bits, + group_size=self.group_size, + is_sym=self.is_sym) def __repr__(self) -> str: return (f"GPTQMarlinConfig(weight_bits={self.weight_bits}, " @@ -117,12 +117,10 @@ def is_marlin_compatible(cls, quant_config: Dict[str, Any]): or desc_act is None): return False - return check_marlin_supported( - num_bits=num_bits, - group_size=group_size, - is_sym=sym, - min_capability=cls.get_min_capability() - ) + return check_marlin_supported(num_bits=num_bits, + group_size=group_size, + is_sym=sym, + min_capability=cls.get_min_capability()) class GPTQMarlinLinearMethod(LinearMethodBase): @@ -154,13 +152,12 @@ def create_weights( group_size = self.quant_config.group_size else: group_size = input_size - + verify_marlin_supports_shape( output_size_per_partition=sum(output_partition_sizes), input_size_per_partition=input_size_per_partition, input_size=input_size, - group_size=group_size - ) + group_size=group_size) # Detect sharding of scales/zp @@ -285,11 +282,10 @@ def create_weights( layer.output_size_per_partition = output_size_per_partition layer.input_size = input_size layer.is_k_full = is_k_full - def process_weights_after_loading(self, layer: Module) -> None: - - # To be used as part of repacking + + # To be used as part of repacking part_size_n = layer.output_size_per_partition part_size_k = layer.input_size_per_partition full_size_k = layer.input_size @@ -337,7 +333,7 @@ def apply( part_size_k = layer.input_size_per_partition out_shape = x.shape[:-1] + (part_size_n, ) - + output = ops.gptq_marlin_gemm( reshaped_x, layer.qweight, diff --git a/vllm/model_executor/layers/quantization/utils/marlin.py b/vllm/model_executor/layers/quantization/utils/marlin.py index 0d1472f8279b1..6024a58e0f127 100644 --- a/vllm/model_executor/layers/quantization/utils/marlin.py +++ b/vllm/model_executor/layers/quantization/utils/marlin.py @@ -5,10 +5,12 @@ from vllm.utils import get_device_capability_stateless + class GPTQMarlinState(enum.Enum): REPACK = enum.auto() READY = enum.auto() + GPTQ_MARLIN_TILE = 16 GPTQ_MARLIN_MIN_THREAD_N = 64 GPTQ_MARLIN_MIN_THREAD_K = 128 @@ -19,6 +21,7 @@ class GPTQMarlinState(enum.Enum): GPTQ_MARLIN_SUPPORTED_SYM = [True] GTPQ_MARLIN_UNSUPPORTED_GROUP_SIZE_ACT_ORDER = [-1] + # Permutations for Marlin scale shuffling def get_scale_perms(num_bits: int): scale_perm: List[int] = [] @@ -42,15 +45,16 @@ def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, return s -def check_marlin_supported(num_bits: int, group_size: int, - is_sym: bool, min_capability: int) -> bool: - - # If the capability of the device is too low, cannot convert. + +def check_marlin_supported(num_bits: int, group_size: int, is_sym: bool, + min_capability: int) -> bool: + + # If the capability of the device is too low, cannot convert. major, minor = get_device_capability_stateless() device_capability = major * 10 + minor if device_capability < min_capability: return False - + return (device_capability >= min_capability and num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS and group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES @@ -82,21 +86,19 @@ def verify_marlin_supports_shape(output_size_per_partition: int, # Validate output_size_per_partition if output_size_per_partition % GPTQ_MARLIN_MIN_THREAD_N != 0: - raise ValueError( - f"Weight output_size_per_partition = " - f"{output_size_per_partition} is not divisible by " - f" min_thread_n = {GPTQ_MARLIN_MIN_THREAD_N}. " - "Consider reducing tensor_parallel_size or running " - "with --quantization gptq.") + raise ValueError(f"Weight output_size_per_partition = " + f"{output_size_per_partition} is not divisible by " + f" min_thread_n = {GPTQ_MARLIN_MIN_THREAD_N}. " + "Consider reducing tensor_parallel_size or running " + "with --quantization gptq.") # Validate input_size_per_partition if input_size_per_partition % GPTQ_MARLIN_MIN_THREAD_K != 0: - raise ValueError( - f"Weight input_size_per_partition = " - f"{input_size_per_partition} is not divisible " - f"by min_thread_k = {GPTQ_MARLIN_MIN_THREAD_K}. " - "Consider reducing tensor_parallel_size or running " - "with --quantization gptq.") + raise ValueError(f"Weight input_size_per_partition = " + f"{input_size_per_partition} is not divisible " + f"by min_thread_k = {GPTQ_MARLIN_MIN_THREAD_K}. " + "Consider reducing tensor_parallel_size or running " + "with --quantization gptq.") if (group_size < input_size and input_size_per_partition % group_size != 0): @@ -106,22 +108,25 @@ def verify_marlin_supports_shape(output_size_per_partition: int, "Consider reducing tensor_parallel_size or running " "with --quantization gptq.") + def get_max_workspace_size(output_size_per_partition: int): - return (output_size_per_partition // + return (output_size_per_partition // GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL + # Newly generated tensors need to replace existing tensors that are # already registered as parameters by vLLM (and won't be freed) -def replace_tensor(layer: torch.nn.Module, name: str, new_t: torch.Tensor) -> None: +def replace_tensor(layer: torch.nn.Module, name: str, + new_t: torch.Tensor) -> None: # It is important to use resize_() here since it ensures # the same buffer is reused getattr(layer, name).resize_(new_t.shape) getattr(layer, name).copy_(new_t) del new_t + # Perform softing -def sort_g_idx(layer: torch.nn.Module, - g_idx_name: str, +def sort_g_idx(layer: torch.nn.Module, g_idx_name: str, g_idx_sort_indices_name: str): g_idx = getattr(layer, g_idx_name) g_idx_sort_indices = torch.argsort(g_idx).to(torch.int) From 75c8a1182ecd57ee355c0ef420b7021a8b2d37c5 Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:18:01 +0000 Subject: [PATCH 010/114] fix models without gidx --- vllm/model_executor/layers/quantization/gptq_marlin.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 40721ec55a745..ebc3d93cb80ee 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -208,7 +208,7 @@ def create_weights( # Activation order g_idx = Parameter( torch.empty( - input_size_per_partition if self.quant_config.desc_act else 0, + input_size_per_partition, dtype=torch.int32, ), requires_grad=False, @@ -294,6 +294,12 @@ def process_weights_after_loading(self, layer: Module) -> None: if self.quant_config.desc_act: # Get sorting based on g_idx sort_g_idx(layer, "g_idx", "g_idx_sort_indices") + else: + # Reset g_idx to empty + layer.g_idx = Parameter( + torch.empty(0, dtype=torch.int), requires_grad=False) + layer.g_idx_sort_indices = Parameter( + torch.empty(0, dtype=torch.int), requires_grad=False) # Repack weights into marlin format marlin_qweight = ops.gptq_marlin_repack( From 525cf0856bc028c78ffb40bd2c5502b15fb42a53 Mon Sep 17 00:00:00 2001 From: "rshaw@neuralmagic.com" Date: Wed, 3 Jul 2024 01:26:51 +0000 Subject: [PATCH 011/114] format --- vllm/model_executor/layers/quantization/gptq_marlin.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index ebc3d93cb80ee..1176cc75cfa64 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -296,10 +296,11 @@ def process_weights_after_loading(self, layer: Module) -> None: sort_g_idx(layer, "g_idx", "g_idx_sort_indices") else: # Reset g_idx to empty - layer.g_idx = Parameter( - torch.empty(0, dtype=torch.int), requires_grad=False) - layer.g_idx_sort_indices = Parameter( - torch.empty(0, dtype=torch.int), requires_grad=False) + layer.g_idx = Parameter(torch.empty(0, dtype=torch.int), + requires_grad=False) + layer.g_idx_sort_indices = Parameter(torch.empty(0, + dtype=torch.int), + requires_grad=False) # Repack weights into marlin format marlin_qweight = ops.gptq_marlin_repack( From 81f028e0fbfbf33f487ada05367469fe15b8bd36 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 02:13:10 +0000 Subject: [PATCH 012/114] fix test failure --- tests/kernels/test_marlin_gemm.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/kernels/test_marlin_gemm.py b/tests/kernels/test_marlin_gemm.py index 05acd626b45eb..ea33e88c95dc7 100644 --- a/tests/kernels/test_marlin_gemm.py +++ b/tests/kernels/test_marlin_gemm.py @@ -6,7 +6,7 @@ import torch from vllm import _custom_ops as ops -from vllm.model_executor.layers.quantization.gptq_marlin import ( +from vllm.model_executor.layers.quantization.utils.marlin import ( GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS) from vllm.model_executor.layers.quantization.gptq_marlin_24 import ( From a8fbe897022358a2bcfde2c59d8a1e47968cccc4 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 02:24:42 +0000 Subject: [PATCH 013/114] fix perms not being on gpu --- vllm/model_executor/layers/quantization/gptq_marlin.py | 7 +++++-- 1 file changed, 5 insertions(+), 2 deletions(-) diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 1176cc75cfa64..ee30312fc32a9 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -296,10 +296,13 @@ def process_weights_after_loading(self, layer: Module) -> None: sort_g_idx(layer, "g_idx", "g_idx_sort_indices") else: # Reset g_idx to empty - layer.g_idx = Parameter(torch.empty(0, dtype=torch.int), + device = layer.qweight.device + layer.g_idx = Parameter(torch.empty(0, dtype=torch.int, + device=device), requires_grad=False) layer.g_idx_sort_indices = Parameter(torch.empty(0, - dtype=torch.int), + dtype=torch.int, + device=device), requires_grad=False) # Repack weights into marlin format From b260c90deb5b4e794cde8f638b3ff06fd95df6f9 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 15:28:57 +0000 Subject: [PATCH 014/114] stage --- .../compressed_tensors/compressed_tensors.py | 13 +- .../schemes/compressed_tensors_scheme.py | 3 +- .../schemes/compressed_tensors_w8a8.py | 14 +- .../schemes/compressed_tensors_w8a8_fp8.py | 149 ++++++---------- .../model_executor/layers/quantization/fp8.py | 159 ++++-------------- 5 files changed, 91 insertions(+), 247 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 10cc5064fb5de..4041040c47781 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -13,12 +13,8 @@ CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( CompressionFormat, QuantizationArgs, QuantizationStrategy, -<<<<<<< HEAD QuantizationType, find_first_name_or_class_match) -======= - find_first_name_or_class_match) from vllm.platforms import current_platform ->>>>>>> main class CompressedTensorsConfig(QuantizationConfig): @@ -157,7 +153,7 @@ def _get_schema(self, weight_quant: BaseModel, if self.quant_format == CompressionFormat.int_quantized.value: if self._is_fp8(weight_quant, input_quant): - return CompressedTensorsFp8() + return CompressedTensorsFp8(input_dynamic=input_quant.dynamic) if self._is_static_tensor_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8StaticTensor( @@ -198,12 +194,9 @@ def __init__(self, quantization_config: CompressedTensorsConfig): self.quantization_config = quantization_config def process_weights_after_loading(self, layer: torch.nn.Module) -> None: -<<<<<<< HEAD - if hasattr(layer.scheme, "process_weights") and layer.scheme.process_weights: + if (hasattr(layer.scheme, "process_after_load") and + layer.scheme.process_after_load): layer.scheme.process_weights_after_loading(layer) -======= - return layer.scheme.process_weights_after_loading(layer) ->>>>>>> main def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py index 119f6cd91bb0c..bd628f770fa00 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py @@ -32,10 +32,9 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): """ raise NotImplementedError - @abstractmethod def process_weights_after_loading(self, layer: torch.nn.Module): """ Called after weight loading is complete for any cleanup that needs to occur. """ - raise NotImplementedError + pass diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 49779057659f0..79c9574bde97a 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -10,17 +10,20 @@ from vllm.model_executor.utils import set_weight_attrs + class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, strategy: str): + def __init__(self, + strategy: QuantizationStrategy): self.strategy = strategy - # Cutlass kernels support only per-tensor and per-channel cases. - # So if we have a fused module (QKV, MLP) with per tensor scales (thus N - # scales being passed to the kernel), we convert to the per-channel case. + # If fused module, we have N scales for N fused weights, but Cutlass kernels + # support only per-tensor and per-channel cases. So we need to handle this + # by converting the the N per-tensor scales to channelwise. def process_weights_after_loading(self, layer: torch.nn.Module) -> None: + is_fused_module = len(self.logical_widths) > 1 if (self.strategy == QuantizationStrategy.TENSOR - and len(self.logical_widths) > 1): + and is_fused_module): # Load the N per-tensor scales into the channelwise buffer. weight_scale_channel = torch.empty( @@ -41,6 +44,7 @@ def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, params_dtype: torch.dtype, weight_loader: Callable, **kwargs): + self.logical_widths = output_partition_sizes # WEIGHT SCALE diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index 918f84efd846b..b1fc52a006ece 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -1,17 +1,22 @@ -from typing import Callable, List, Optional, Tuple, Union +from typing import Callable, List, Optional import torch -from torch.nn import Parameter, Module -from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) +from vllm.model_executor.layers.quantization.utils.fp8_utils import ( + fp8_apply, requantize_with_max_scale, create_scale_param, + cutlass_fp8_supported) from vllm.model_executor.utils import set_weight_attrs __all__ = ["CompressedTensorsW8A8FP8"] -class CompressedTensorsFp8(CompressedTensorsScheme): +class CompressedTensorsFp8(CompressedTensorsScheme): + def __init__(self, input_dynamic: bool): + self.input_dynamic = input_dynamic + self.cutlass_fp8_supported = cutlass_fp8_supported() + def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], @@ -20,116 +25,60 @@ def create_weights(self, weight_loader: Callable, **kwargs): - self.process_weights=True + del params_dtype + output_size_per_partition = sum(output_partition_sizes) layer.logical_widths = output_partition_sizes + layer.process_after_load = True - weight = Parameter(torch.empty(output_size_per_partition, - input_size_per_partition, - dtype=torch.float8_e4m3fn), - requires_grad=False) + # WEIGHT + weight = torch.nn.Parameter(torch.empty(output_size_per_partition, + input_size_per_partition, + dtype=torch.float8_e4m3fn), + requires_grad=False) layer.register_parameter("weight", weight) set_weight_attrs(weight, { - "input_dim": 1, - "output_dim": 0, + "input_dim": 1, "output_dim": 0, "weight_loader": weight_loader, }) - weight_scale = Parameter(torch.empty(len(output_partition_sizes), - dtype=torch.float32), - requires_grad=False) - + # WEIGHT SCALE + weight_scale = create_scale_param(output_partition_sizes, + weight_loader=weight_loader) layer.register_parameter("weight_scale", weight_scale) - set_weight_attrs(weight_scale, { - "weight_loader": weight_loader, - "ignore_warning": True, - "fp8_scales_shard_indexer": self.scales_shard_indexer, - }) - input_scale = Parameter(torch.empty(len(output_partition_sizes), - dtype=torch.float32), - requires_grad=False) - - layer.register_parameter("input_scale", input_scale) - set_weight_attrs(input_scale, { - "weight_loader": weight_loader, - "ignore_warning": True, - "fp8_scales_shard_indexer": self.scales_shard_indexer, - }) - - - def scales_shard_indexer(self, - param: torch.Tensor, - loaded_weight: torch.Tensor, - shard_id: Union[str, int]) -> Tuple[torch.Tensor, torch.Tensor]: - - qkv_idxs = {"q": 0, "k": 1, "v": 2} - - if isinstance(shard_id, int): - pass - elif isinstance(shard_id, str): - if shard_id not in qkv_idxs: - raise ValueError(f"Unknown shard_id: {shard_id}") - shard_id = qkv_idxs[shard_id] - else: - ValueError(f"Shard id must be int or str but got {type(shard_id)}") - - return param[shard_id].reshape(-1), loaded_weight + # INPUT SCALE + if not self.input_dynamic: + input_scale = create_scale_param(output_partition_sizes, + weight_loader=weight_loader) + layer.register_parameter("input_scale", input_scale) def process_weights_after_loading(self, layer) -> None: - # WEIGHT_SCALE / WEIGHT - # Loop over logical weights, requantizing with single scale. - max_w_scale = layer.weight_scale.max() - start = 0 - for idx, logical_width in enumerate(layer.logical_widths): - end = start + logical_width - weight_dq = per_tensor_dequantize(layer.weight[start:end, :], - layer.weight_scale[idx]) - - layer.weight[start:end, :] = per_tensor_quantize( - weight_dq, layer.weight_scale.max()) - start = end - layer.weight_scale = Parameter(max_w_scale, requires_grad=False) + # Dequant -> Quant with max scale. + max_w_scale, weight = requantize_with_max_scale( + weight=layer.weight, + weight_scale=layer.weight_scale, + logical_widths=layer.logical_widths, + ) - # WEIGHT - # Transpose weight for passing to torch._scaled_mm - weight = layer.weight - layer.weight = Parameter(weight.t(), requires_grad=False) - layer.input_scale = Parameter(layer.input_scale.max(), - requires_grad=False) + # Update layer with new values. + layer.weight = torch.nn.Parameter(weight.t(), requires_grad=False) + layer.weight_scale = torch.nn.Parameter(max_w_scale, requires_grad=False) + if self.input_dynamic: + layer.input_scale = None + else: + layer.input_scale = torch.nn.Parameter(layer.input_scale.max(), + requires_grad=False) def apply_weights(self, - layer: torch.nn.Module, - x: torch.Tensor, - bias: Optional[torch.Tensor] = None) -> torch.Tensor: + layer: torch.nn.Module, + x: torch.Tensor, + bias: Optional[torch.Tensor] = None) -> torch.Tensor: - assert bias is None - - qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale) - - # Fused GEMM_DQ - output = ops.cutlass_scaled_mm( - qinput, - layer.weight, - out_dtype=x.dtype, - scale_a=x_scale, - scale_b=layer.weight_scale, - ) - - return torch.narrow(output, 0, 0, x.shape[0]) - - -def per_tensor_quantize(tensor: torch.Tensor, - inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: - finfo = torch.finfo(torch.float8_e4m3fn) - qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) - return qweight.to(torch.float8_e4m3fn) - - -def per_tensor_dequantize( - tensor: torch.Tensor, inv_scale: Union[float, - torch.Tensor]) -> torch.Tensor: - fake_qweight = tensor.to(torch.float16) - dq_weight = fake_qweight * inv_scale - return dq_weight + return fp8_apply(input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, + bias=bias, + cutlass_fp8_supported=self.cutlass_fp8_supported) diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index 6d942fa611c7b..9d996fe9bc698 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -9,24 +9,19 @@ from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase, fused_moe) from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase +from vllm.model_executor.layers.quantization.utils.fp8_utils import ( + fp8_apply, requantize_with_max_scale, all_close_1d, + per_tensor_dequantize, per_tensor_quantize, create_scale_param, + cutlass_fp8_supported) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) from vllm.model_executor.utils import set_weight_attrs -from vllm.platforms import current_platform from vllm.utils import print_warning_once ACTIVATION_SCHEMES = ["static", "dynamic"] logger = init_logger(__name__) - -def cutlass_fp8_supported() -> bool: - capability = current_platform.get_device_capability() - capability = capability[0] * 10 + capability[1] - - return ops.cutlass_scaled_mm_supports_fp8(capability) - - class Fp8Config(QuantizationConfig): """Config class for FP8.""" @@ -106,23 +101,6 @@ def __init__(self, quant_config: Fp8Config): self.quant_config = quant_config self.cutlass_fp8_supported = cutlass_fp8_supported() - def _create_scale_param( - self, - scale_name: str, - layer: torch.nn.Module, - output_partition_sizes: List[int], - **extra_weight_attrs, - ) -> None: - scale = Parameter(torch.empty(len(output_partition_sizes), - dtype=torch.float32), - requires_grad=False) - scale[:] = torch.finfo(torch.float8_e4m3fn).min - layer.register_parameter(scale_name, scale) - set_weight_attrs(scale, { - **extra_weight_attrs, - "needs_scalar_to_array": True, - }) - def create_weights( self, layer: torch.nn.Module, @@ -158,121 +136,62 @@ def create_weights( # Otherwise, wait until process_weights_after_loading. if self.quant_config.is_checkpoint_fp8_serialized: # WEIGHT SCALE - self._create_scale_param( - scale_name="weight_scale", - layer=layer, - output_partition_sizes=output_partition_sizes, - **extra_weight_attrs) + scale = create_scale_param(output_partition_sizes, + **extra_weight_attrs) + layer.register_parameter("weight_scale", scale) # INPUT ACTIVATION SCALE if self.quant_config.activation_scheme == "static": - self._create_scale_param( - scale_name="input_scale", - layer=layer, - output_partition_sizes=output_partition_sizes, - **extra_weight_attrs) + scale = create_scale_param(output_partition_sizes, + **extra_weight_attrs) + layer.register_parameter("input_scale", scale) def process_weights_after_loading(self, layer: Module) -> None: if (not hasattr(layer, "process_after_load") or not layer.process_after_load): return - # If checkpoint is fp/bf16 (not serialized fp8), quantize the weights. + # If checkpoint not serialized fp8, quantize the weights. if not self.quant_config.is_checkpoint_fp8_serialized: qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None) + + # Update the layer with the new values. layer.weight = Parameter(qweight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) - layer.logical_widths = None layer.input_scale = None return # If checkpoint is fp8, requantize the separately quantized logical # weights into a single fp8 weight with a single weight scale. else: - # WEIGHT_SCALE / WEIGHT - # Loop over logical weights, requantizing with single scale. - max_w_scale = layer.weight_scale.max() - - # QKV / MLP is fused in the on disk checkpoint if any of the - # weight scales are still set to the default since we initialize - # N weight scales for N shards but we only load 1 weight scale - # from disk in this case. As a result, we skip dequant -> requant - # since we already have quantized QKV together. - # Sample Model with fused checkpoint: - # * nm-testing/Phi-3-mini-128k-instruct-FP8 - unfused_module_in_checkpoint = ( - layer.weight_scale[-1] > torch.finfo(torch.float8_e4m3fn).min) - - if unfused_module_in_checkpoint: - start = 0 - for idx, logical_width in enumerate(layer.logical_widths): - end = start + logical_width - weight_dq = per_tensor_dequantize( - layer.weight[start:end, :], layer.weight_scale[idx]) - - layer.weight[start:end, :] = per_tensor_quantize( - weight_dq, layer.weight_scale.max()) - start = end - layer.weight_scale = Parameter(max_w_scale, requires_grad=False) + # Dequant -> Quant with max scale. + max_w_scale, weight = requantize_with_max_scale( + weight=layer.weight, + weight_scale=layer.weight_scale, + logical_widths=layer.logical_widths, + ) - # WEIGHT - # Transpose weight for passing to torch._scaled_mm - weight = layer.weight + # Update layer with new values. layer.weight = Parameter(weight.t(), requires_grad=False) - - # INPUT ACTIVATION SCALE - # Dynamic: set to None (required input to ops.scaled_fp8_quant). - # Static: set to max of the input_scales (since they are equal). - if self.quant_config.activation_scheme == "dynamic": - layer.input_scale = None - elif self.quant_config.activation_scheme == "static": + layer.weight_scale = Parameter(max_w_scale, requires_grad=False) + if self.quant_config.activation_scheme == "static": layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False) else: - raise ValueError( - f"Unknown scheme {self.quant_config.activation_scheme}") + layer.input_scale = None def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - # ops.scaled_fp8_quant supports both dynamic and static quant. - # If dynamic, layer.input_scale is None and x_scale computed from x. - # If static, layer.input_scale is scalar and x_scale is input_scale. - - if bias is None and self.cutlass_fp8_supported: - qinput, x_scale = ops.scaled_fp8_quant(x, layer.input_scale) - - # Fused GEMM_DQ - output = ops.cutlass_scaled_mm( - qinput, - layer.weight, - out_dtype=x.dtype, - scale_a=x_scale, - scale_b=layer.weight_scale, - ) - - else: - qinput, x_scale = ops.scaled_fp8_quant(x, - layer.input_scale, - batch_dim_padding=17) - - # Fused GEMM_DQ -- note we padded the input above because - # torch._scaled_mm is more performant for matrices with - # batch dimension > 16. Note that this could change - # in the future. - output, _ = torch._scaled_mm( - qinput, - layer.weight, - out_dtype=x.dtype, - scale_a=x_scale, - scale_b=layer.weight_scale, - bias=bias, - ) - - return torch.narrow(output, 0, 0, x.shape[0]) + return fp8_apply(input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, + bias=bias, + cutlass_fp8_supported=self.cutlass_fp8_supported) class Fp8MoEMethod(FusedMoEMethodBase): @@ -492,23 +411,3 @@ def process_weights_after_loading(self, layer: Module) -> None: "cause accuracy issues. Please make sure kv-cache scaling " "factor is available in the fp8 checkpoint.") del layer.kv_scale - - -def per_tensor_quantize(tensor: torch.Tensor, - inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: - finfo = torch.finfo(torch.float8_e4m3fn) - qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) - return qweight.to(torch.float8_e4m3fn) - - -def per_tensor_dequantize( - tensor: torch.Tensor, inv_scale: Union[float, - torch.Tensor]) -> torch.Tensor: - fake_qweight = tensor.to(torch.float16) - dq_weight = fake_qweight * inv_scale - return dq_weight - - -def all_close_1d(x: torch.Tensor) -> bool: - assert len(x.shape) == 1 - return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0])) From c8e97b10cc6032a8cd0ff72851070d6a9eb01c71 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 15:31:35 +0000 Subject: [PATCH 015/114] updated --- .../quantization/compressed_tensors/compressed_tensors.py | 4 +--- .../compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py | 1 - 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 4041040c47781..3f0e95852fa4c 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -194,9 +194,7 @@ def __init__(self, quantization_config: CompressedTensorsConfig): self.quantization_config = quantization_config def process_weights_after_loading(self, layer: torch.nn.Module) -> None: - if (hasattr(layer.scheme, "process_after_load") and - layer.scheme.process_after_load): - layer.scheme.process_weights_after_loading(layer) + layer.scheme.process_weights_after_loading(layer) def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index b1fc52a006ece..dd60b249ee58d 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -29,7 +29,6 @@ def create_weights(self, output_size_per_partition = sum(output_partition_sizes) layer.logical_widths = output_partition_sizes - layer.process_after_load = True # WEIGHT weight = torch.nn.Parameter(torch.empty(output_size_per_partition, From e58063d40e45bedf505514a493e731263d4d0902 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 15:32:36 +0000 Subject: [PATCH 016/114] nit --- vllm/model_executor/layers/linear.py | 1 + 1 file changed, 1 insertion(+) diff --git a/vllm/model_executor/layers/linear.py b/vllm/model_executor/layers/linear.py index 06d0048af5deb..3cc257834033a 100644 --- a/vllm/model_executor/layers/linear.py +++ b/vllm/model_executor/layers/linear.py @@ -738,6 +738,7 @@ def weight_loader(self, param: Parameter, loaded_weight: torch.Tensor): # have a shape (such as in the case of AutoFP8). if len(loaded_weight.shape) == 0: loaded_weight = loaded_weight.reshape(1) + assert param_data.shape == loaded_weight.shape param_data.copy_(loaded_weight) From 383e471449a7646d3be5965b273ca962ab66ca98 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 15:48:55 +0000 Subject: [PATCH 017/114] added --- .../model_executor/layers/quantization/fp8.py | 9 - .../layers/quantization/utils/fp8_utils.py | 109 +++ vllm/model_executor/models/mixtral-old.py | 643 ++++++++++++++++++ 3 files changed, 752 insertions(+), 9 deletions(-) create mode 100644 vllm/model_executor/layers/quantization/utils/fp8_utils.py create mode 100644 vllm/model_executor/models/mixtral-old.py diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index 9d996fe9bc698..6ef29afa9d6ec 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -114,7 +114,6 @@ def create_weights( del input_size, output_size output_size_per_partition = sum(output_partition_sizes) - layer.process_after_load = True layer.logical_widths = output_partition_sizes # WEIGHT @@ -147,9 +146,6 @@ def create_weights( layer.register_parameter("input_scale", scale) def process_weights_after_loading(self, layer: Module) -> None: - if (not hasattr(layer, "process_after_load") - or not layer.process_after_load): - return # If checkpoint not serialized fp8, quantize the weights. if not self.quant_config.is_checkpoint_fp8_serialized: @@ -214,8 +210,6 @@ def create_weights(self, layer: Module, num_experts: int, hidden_size: int, intermediate_size: int, params_dtype: torch.dtype, **extra_weight_attrs): - layer.process_after_load = True - if self.quant_config.is_checkpoint_fp8_serialized: params_dtype = torch.float8_e4m3fn @@ -280,9 +274,6 @@ def create_weights(self, layer: Module, num_experts: int, hidden_size: int, layer.a2_scale = None def process_weights_after_loading(self, layer: Module) -> None: - if (not hasattr(layer, "process_after_load") - or not layer.process_after_load): - return # If checkpoint is fp16, quantize in place. if not self.quant_config.is_checkpoint_fp8_serialized: diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py new file mode 100644 index 0000000000000..36337dca2cc63 --- /dev/null +++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py @@ -0,0 +1,109 @@ +import torch +from typing import Optional, List, Tuple, Union + +from vllm import _custom_ops as ops +from vllm.platforms import current_platform +from vllm.model_executor.utils import set_weight_attrs + +def cutlass_fp8_supported() -> bool: + capability = current_platform.get_device_capability() + capability = capability[0] * 10 + capability[1] + + return ops.cutlass_scaled_mm_supports_fp8(capability) + +def per_tensor_quantize(tensor: torch.Tensor, + inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: + finfo = torch.finfo(torch.float8_e4m3fn) + qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) + return qweight.to(torch.float8_e4m3fn) + +def per_tensor_dequantize( + tensor: torch.Tensor, inv_scale: Union[float, + torch.Tensor]) -> torch.Tensor: + fake_qweight = tensor.to(torch.float16) + dq_weight = fake_qweight * inv_scale + return dq_weight + +def all_close_1d(x: torch.Tensor) -> bool: + assert len(x.shape) == 1 + return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0])) + +def create_scale_param(output_partition_sizes: List[int], + **extra_weight_attrs) -> torch.nn.Parameter: + scale = torch.nn.Parameter(torch.empty(len(output_partition_sizes), + dtype=torch.float32), + requires_grad=False) + scale[:] = torch.finfo(torch.float8_e4m3fn).min + set_weight_attrs(scale, { + **extra_weight_attrs, + "needs_scalar_to_array": True, + }) + return scale + +def requantize_with_max_scale( + weight: torch.Tensor, + weight_scale: torch.Tensor, + logical_widths: List[int] +) -> Tuple[torch.Tensor, torch.Tensor]: + # Max scale to be used for requanitzation. + max_w_scale = weight_scale.max() + + # QKV / MLP is fused in the on disk checkpoint if any of the + # weight scales are still set to the default since we initialize + # N weight scales for N shards but we only load 1 weight scale + # from disk in this case. Skip requantization in this case (since) + # we already are quantized with the single scale. + # * Sample Model: nm-testing/Phi-3-mini-128k-instruct-FP8 + unfused_module_in_checkpoint = ( + weight_scale[-1] > torch.finfo(torch.float8_e4m3fn).min) + + # If unfused checkpoint, need requanize with the single scale. + if unfused_module_in_checkpoint: + start = 0 + for idx, logical_width in enumerate(logical_widths): + end = start + logical_width + weight_dq = per_tensor_dequantize(weight[start:end, :], weight_scale[idx]) + weight[start:end, :] = per_tensor_quantize(weight_dq, max_w_scale) + start = end + + return max_w_scale, weight + +def fp8_apply( + input: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + input_scale: torch.Tensor, + bias: Optional[torch.Tensor] = None, + cutlass_fp8_supported: bool = True, +): + # ops.scaled_fp8_quant supports both dynamic and static quant. + # If dynamic, layer.input_scale is None and x_scale computed from x. + # If static, layer.input_scale is scalar and x_scale is input_scale. + + if bias is None and cutlass_fp8_supported: + qinput, x_scale = ops.scaled_fp8_quant(input, input_scale) + + # Fused GEMM_DQ + output = ops.cutlass_scaled_mm(qinput, + weight, + out_dtype=input.dtype, + scale_a=x_scale, + scale_b=weight_scale) + + else: + qinput, x_scale = ops.scaled_fp8_quant(input, input_scale, + batch_dim_padding=17) + + # Fused GEMM_DQ -- note we padded the input above because + # torch._scaled_mm is more performant for matrices with + # batch dimension > 16. Note that this could change + # in the future. + output, _ = torch._scaled_mm(qinput, + weight, + out_dtype=input.dtype, + scale_a=x_scale, + scale_b=weight_scale, + bias=bias) + + return torch.narrow(output, 0, 0, input.shape[0]) + \ No newline at end of file diff --git a/vllm/model_executor/models/mixtral-old.py b/vllm/model_executor/models/mixtral-old.py new file mode 100644 index 0000000000000..a662db6d28d00 --- /dev/null +++ b/vllm/model_executor/models/mixtral-old.py @@ -0,0 +1,643 @@ +# coding=utf-8 +# Adapted from +# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py +# Copyright 2023 The vLLM team. +# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""Inference-only Mixtral model.""" +from typing import Iterable, List, Optional, Tuple + +import torch +from torch import nn +from transformers import MixtralConfig + +from vllm import _custom_ops as ops +from vllm.attention import Attention, AttentionMetadata +from vllm.config import CacheConfig, LoRAConfig +from vllm.distributed import (get_tensor_model_parallel_rank, + get_tensor_model_parallel_world_size, + tensor_model_parallel_all_reduce) +from vllm.model_executor.layers.fused_moe import fused_moe +from vllm.model_executor.layers.layernorm import RMSNorm +from vllm.model_executor.layers.linear import (QKVParallelLinear, + ReplicatedLinear, + RowParallelLinear) +from vllm.model_executor.layers.logits_processor import LogitsProcessor +from vllm.model_executor.layers.quantization.base_config import ( + QuantizationConfig) +from vllm.model_executor.layers.quantization.fp8 import (Fp8Config, + per_tensor_dequantize, + per_tensor_quantize) +from vllm.model_executor.layers.rotary_embedding import get_rope +from vllm.model_executor.layers.sampler import Sampler +from vllm.model_executor.layers.vocab_parallel_embedding import ( + DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) +from vllm.model_executor.model_loader.weight_utils import default_weight_loader +from vllm.model_executor.sampling_metadata import SamplingMetadata +from vllm.model_executor.utils import set_weight_attrs +from vllm.sequence import SamplerOutput +from vllm.utils import print_warning_once + +from .interfaces import SupportsLoRA + + +class MixtralMoE(nn.Module): + """A tensor-parallel MoE implementation for Mixtral that shards each expert + across all ranks. + + Each expert's weights are sharded across all ranks and a fused MoE + kernel is used for the forward pass, and finally we reduce the outputs + across ranks. + """ + + def __init__( + self, + num_experts: int, + top_k: int, + hidden_size: int, + intermediate_size: int, + params_dtype: Optional[torch.dtype] = None, + tp_size: Optional[int] = None, + quant_config: Optional[QuantizationConfig] = None, + ): + super().__init__() + self.tp_size = tp_size or get_tensor_model_parallel_world_size() + self.num_total_experts = num_experts + self.top_k = top_k + self.hidden_size = hidden_size + self.intermediate_size = intermediate_size // self.tp_size + self.quant_config = quant_config + + # FIXME(pcmoritz): Make this more general to support different + # quantization schemes + self.use_fp8 = isinstance(quant_config, Fp8Config) + + if params_dtype is None: + params_dtype = torch.get_default_dtype() + self.params_dtype = params_dtype + + # Gate always runs at half / full precision for now. + self.gate = ReplicatedLinear(self.hidden_size, + self.num_total_experts, + bias=False, + params_dtype=self.params_dtype, + quant_config=None) + + if self.use_fp8 and self.quant_config.is_checkpoint_fp8_serialized: + params_dtype = torch.float8_e4m3fn + + self.w13_weight = nn.Parameter(torch.empty(self.num_total_experts, + 2 * self.intermediate_size, + self.hidden_size, + dtype=params_dtype), + requires_grad=False) + self.w2_weight = nn.Parameter(torch.empty(self.num_total_experts, + self.hidden_size, + self.intermediate_size, + dtype=params_dtype), + requires_grad=False) + + set_weight_attrs(self.w13_weight, { + "weight_loader": self.weight_loader, + }) + set_weight_attrs(self.w2_weight, { + "weight_loader": self.weight_loader, + }) + + # Used for fp8. + self.w13_scale = None + self.w2_scale = None + self.a13_scale = None + self.a2_scale = None + + if self.use_fp8: + # WEIGHT_SCALE (for fp8) + # Allocate 2 scales for w1 and w3 respectively. + # They will be combined to a single scale after weight loading. + self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts, + 2, + dtype=torch.float32), + requires_grad=False) + self.w2_scale = nn.Parameter(torch.ones(self.num_total_experts, + dtype=torch.float32), + requires_grad=False) + + # If loading fp8 checkpoint, pass the weight loaders. + # If loading an fp16 checkpoint, do not (we will quantize in + # process_weights_after_loading() + if quant_config.is_checkpoint_fp8_serialized: + set_weight_attrs(self.w13_scale, { + "weight_loader": self.weight_loader, + }) + set_weight_attrs(self.w2_scale, { + "weight_loader": self.weight_loader, + }) + + # INPUT_SCALE (for fp8) + if quant_config.activation_scheme == "static": + if not quant_config.is_checkpoint_fp8_serialized: + raise ValueError( + "Found static activation scheme for checkpoint that " + "was not serialized fp8.") + self.a13_scale = nn.Parameter(torch.ones( + self.num_total_experts, dtype=torch.float32), + requires_grad=False) + self.a2_scale = nn.Parameter(torch.ones(self.num_total_experts, + dtype=torch.float32), + requires_grad=False) + + set_weight_attrs(self.a13_scale, { + "weight_loader": self.weight_loader, + }) + set_weight_attrs(self.a2_scale, { + "weight_loader": self.weight_loader, + }) + + def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, + weight_name: str, expert_id: int): + tp_rank = get_tensor_model_parallel_rank() + param_data = param.data + shard_size = self.intermediate_size + shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size) + if weight_name.endswith("w1.weight"): + param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :] + if weight_name.endswith("w3.weight"): + param_data[expert_id, + shard_size:2 * shard_size, :] = loaded_weight[shard, :] + if weight_name.endswith("w2.weight"): + param_data[expert_id, :, :] = loaded_weight[:, shard] + + # Loading scales + if "input_scale" in weight_name or "w2.weight_scale" in weight_name: + if param_data[expert_id] != 1 and (param_data[expert_id] - + loaded_weight).abs() > 1e-5: + raise ValueError( + "input_scales of w1 and w3 of a layer " + f"must be equal. But got {param_data[expert_id]} " + f"vs. {loaded_weight}") + param_data[expert_id] = loaded_weight + elif "weight_scale" in weight_name: + # We have to keep the weight scales of w1 and w3 because + # we need to re-quantize w1/w3 weights after weight loading. + assert "w1" in weight_name or "w3" in weight_name + shard_id = 0 if "w1" in weight_name else 1 + param_data[expert_id][shard_id] = loaded_weight + + def process_weights_after_loading(self): + # Fp8 is the only case where we need to process after loading. + if not self.use_fp8: + return + + # If checkpoint is fp16, quantize here. + if not self.quant_config.is_checkpoint_fp8_serialized: + w13_weight = torch.empty_like(self.w13_weight.data, + dtype=torch.float8_e4m3fn) + w2_weight = torch.empty_like(self.w2_weight.data, + dtype=torch.float8_e4m3fn) + + # Re-initialize w13_scale because we directly quantize + # merged w13 weights and generate a single scaling factor. + self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts, + dtype=torch.float32), + requires_grad=False) + for expert in range(self.num_total_experts): + w13_weight[expert, :, :], self.w13_scale[ + expert] = ops.scaled_fp8_quant( + self.w13_weight.data[expert, :, :]) + w2_weight[expert, :, :], self.w2_scale[ + expert] = ops.scaled_fp8_quant( + self.w2_weight.data[expert, :, :]) + self.w13_weight = nn.Parameter(w13_weight, requires_grad=False) + self.w2_weight = nn.Parameter(w2_weight, requires_grad=False) + + else: + # If checkpoint is fp8 + static, cleanup input_scales. + # Since state_dict has an input_scale per expert but our kernels + # are passed one input_scale shared across all experts. + if self.quant_config.activation_scheme == "static": + if self.a13_scale is None or self.a2_scale is None: + raise ValueError( + "QuantConfig has static quantization, but found " + "activation scales are None.") + + if (not all_close_1d(self.a13_scale) + or not all_close_1d(self.a2_scale)): + print_warning_once( + "Found input_scales that are not equal for " + "fp8 MoE layer. Using the maximum across experts " + "for each layer. ") + + self.a13_scale = nn.Parameter(self.a13_scale.max(), + requires_grad=False) + self.a2_scale = nn.Parameter(self.a2_scale.max(), + requires_grad=False) + + assert self.w13_scale is not None + shard_size = self.intermediate_size + max_w13_scales = self.w13_scale.max(dim=1).values + for expert_id in range(self.num_total_experts): + start = 0 + for shard_id in range(2): + dq_weight = per_tensor_dequantize( + self.w13_weight[expert_id][start:start + + shard_size, :], + self.w13_scale[expert_id][shard_id]) + self.w13_weight[expert_id][ + start:start + shard_size, :] = per_tensor_quantize( + dq_weight, max_w13_scales[expert_id]) + start += shard_size + + self.w13_scale = nn.Parameter(max_w13_scales, requires_grad=False) + + def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: + num_tokens, hidden_size = hidden_states.shape + hidden_states = hidden_states.view(-1, self.hidden_size) + # router_logits: (num_tokens, n_experts) + router_logits, _ = self.gate(hidden_states) + final_hidden_states = fused_moe(hidden_states, + self.w13_weight, + self.w2_weight, + router_logits, + self.top_k, + renormalize=True, + inplace=True, + use_fp8=self.use_fp8, + w1_scale=self.w13_scale, + w2_scale=self.w2_scale, + a1_scale=self.a13_scale, + a2_scale=self.a2_scale) + + if self.tp_size > 1: + final_hidden_states = tensor_model_parallel_all_reduce( + final_hidden_states) + + return final_hidden_states.view(num_tokens, hidden_size) + + +class MixtralAttention(nn.Module): + + def __init__( + self, + hidden_size: int, + num_heads: int, + num_kv_heads: int, + max_position: int = 4096 * 32, + rope_theta: float = 10000, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + ) -> None: + super().__init__() + self.hidden_size = hidden_size + tp_size = get_tensor_model_parallel_world_size() + self.total_num_heads = num_heads + assert self.total_num_heads % tp_size == 0 + self.num_heads = self.total_num_heads // tp_size + self.total_num_kv_heads = num_kv_heads + if self.total_num_kv_heads >= tp_size: + # Number of KV heads is greater than TP size, so we partition + # the KV heads across multiple tensor parallel GPUs. + assert self.total_num_kv_heads % tp_size == 0 + else: + # Number of KV heads is less than TP size, so we replicate + # the KV heads across multiple tensor parallel GPUs. + assert tp_size % self.total_num_kv_heads == 0 + self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) + self.head_dim = hidden_size // self.total_num_heads + self.q_size = self.num_heads * self.head_dim + self.kv_size = self.num_kv_heads * self.head_dim + self.scaling = self.head_dim**-0.5 + self.rope_theta = rope_theta + + self.qkv_proj = QKVParallelLinear( + hidden_size, + self.head_dim, + self.total_num_heads, + self.total_num_kv_heads, + bias=False, + quant_config=quant_config, + ) + self.o_proj = RowParallelLinear( + self.total_num_heads * self.head_dim, + hidden_size, + bias=False, + quant_config=quant_config, + ) + self.rotary_emb = get_rope( + self.head_dim, + rotary_dim=self.head_dim, + max_position=max_position, + base=int(self.rope_theta), + is_neox_style=True, + ) + self.attn = Attention(self.num_heads, + self.head_dim, + self.scaling, + num_kv_heads=self.num_kv_heads, + cache_config=cache_config, + quant_config=quant_config) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: torch.Tensor, + attn_metadata: AttentionMetadata, + ) -> torch.Tensor: + qkv, _ = self.qkv_proj(hidden_states) + q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) + q, k = self.rotary_emb(positions, q, k) + attn_output = self.attn(q, k, v, kv_cache, attn_metadata) + output, _ = self.o_proj(attn_output) + return output + + +class MixtralDecoderLayer(nn.Module): + + def __init__( + self, + config: MixtralConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + ) -> None: + super().__init__() + self.hidden_size = config.hidden_size + # Requires transformers > 4.32.0 + rope_theta = getattr(config, "rope_theta", 10000) + self.self_attn = MixtralAttention( + hidden_size=self.hidden_size, + num_heads=config.num_attention_heads, + max_position=config.max_position_embeddings, + num_kv_heads=config.num_key_value_heads, + rope_theta=rope_theta, + cache_config=cache_config, + quant_config=quant_config) + self.block_sparse_moe = MixtralMoE( + num_experts=config.num_local_experts, + top_k=config.num_experts_per_tok, + hidden_size=config.hidden_size, + intermediate_size=config.intermediate_size, + quant_config=quant_config) + self.input_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(config.hidden_size, + eps=config.rms_norm_eps) + + def forward( + self, + positions: torch.Tensor, + hidden_states: torch.Tensor, + kv_cache: torch.Tensor, + attn_metadata: AttentionMetadata, + residual: Optional[torch.Tensor], + ) -> torch.Tensor: + # Self Attention + if residual is None: + residual = hidden_states + hidden_states = self.input_layernorm(hidden_states) + else: + hidden_states, residual = self.input_layernorm( + hidden_states, residual) + hidden_states = self.self_attn( + positions=positions, + hidden_states=hidden_states, + kv_cache=kv_cache, + attn_metadata=attn_metadata, + ) + + # Fully Connected + hidden_states, residual = self.post_attention_layernorm( + hidden_states, residual) + hidden_states = self.block_sparse_moe(hidden_states) + return hidden_states, residual + + +class MixtralModel(nn.Module): + + def __init__( + self, + config: MixtralConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + lora_config: Optional[LoRAConfig] = None, + ) -> None: + super().__init__() + self.padding_idx = config.pad_token_id + lora_vocab = (lora_config.lora_extra_vocab_size * + (lora_config.max_loras or 1)) if lora_config else 0 + self.vocab_size = config.vocab_size + lora_vocab + self.org_vocab_size = config.vocab_size + + self.embed_tokens = VocabParallelEmbedding( + self.vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + ) + self.layers = nn.ModuleList([ + MixtralDecoderLayer(config, + cache_config, + quant_config=quant_config) + for _ in range(config.num_hidden_layers) + ]) + self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + ) -> torch.Tensor: + hidden_states = self.embed_tokens(input_ids) + residual = None + for i in range(len(self.layers)): + layer = self.layers[i] + hidden_states, residual = layer(positions, hidden_states, + kv_caches[i], attn_metadata, + residual) + hidden_states, _ = self.norm(hidden_states, residual) + return hidden_states + + +class MixtralForCausalLM(nn.Module, SupportsLoRA): + fall_back_to_pt_during_load = False + + packed_modules_mapping = { + "qkv_proj": [ + "q_proj", + "k_proj", + "v_proj", + ], + } + + # LoRA specific attributes + supported_lora_modules = [ + "qkv_proj", + "o_proj", + "embed_tokens", + "lm_head", + ] + embedding_modules = { + "embed_tokens": "input_embeddings", + "lm_head": "output_embeddings", + } + embedding_padding_modules = ["lm_head"] + + def __init__( + self, + config: MixtralConfig, + cache_config: Optional[CacheConfig] = None, + quant_config: Optional[QuantizationConfig] = None, + lora_config: Optional[LoRAConfig] = None, + ) -> None: + super().__init__() + + self.config = config + self.lora_config = lora_config + + self.model = MixtralModel(config, + cache_config, + quant_config, + lora_config=lora_config) + self.unpadded_vocab_size = config.vocab_size + if lora_config: + self.unpadded_vocab_size += lora_config.lora_extra_vocab_size + self.lm_head = ParallelLMHead( + self.unpadded_vocab_size, + config.hidden_size, + org_num_embeddings=config.vocab_size, + padding_size=DEFAULT_VOCAB_PADDING_SIZE + # We need bigger padding if using lora for kernel + # compatibility + if not lora_config else lora_config.lora_vocab_padding_size, + ) + self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, + config.vocab_size) + self.sampler = Sampler() + + def forward( + self, + input_ids: torch.Tensor, + positions: torch.Tensor, + kv_caches: List[torch.Tensor], + attn_metadata: AttentionMetadata, + ) -> torch.Tensor: + hidden_states = self.model(input_ids, positions, kv_caches, + attn_metadata) + return hidden_states + + def compute_logits(self, hidden_states: torch.Tensor, + sampling_metadata: SamplingMetadata) -> torch.Tensor: + logits = self.logits_processor(self.lm_head.weight, hidden_states, + sampling_metadata) + return logits + + def sample( + self, + logits: Optional[torch.Tensor], + sampling_metadata: SamplingMetadata, + ) -> Optional[SamplerOutput]: + next_tokens = self.sampler(logits, sampling_metadata) + return next_tokens + + def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): + stacked_params_mapping = [ + # (param_name, shard_name, shard_id) + ("qkv_proj", "q_proj", "q"), + ("qkv_proj", "k_proj", "k"), + ("qkv_proj", "v_proj", "v"), + ] + + expert_params_mapping = [ + # These are the weight scales for the experts + # (param_name, weight_name, expert_id) + ("w13_scale" if weight_name in ["w1", "w3"] else "w2_scale", + f"experts.{expert_id}.{weight_name}.weight_scale", expert_id) + for expert_id in range(self.config.num_local_experts) + for weight_name in ["w1", "w2", "w3"] + ] + [ + # These are the weights for the experts + # (param_name, weight_name, expert_id) + ("w13_weight" if weight_name in ["w1", "w3"] else "w2_weight", + f"experts.{expert_id}.{weight_name}.weight", expert_id) + for expert_id in range(self.config.num_local_experts) + for weight_name in ["w1", "w2", "w3"] + ] + [ + # These are the activation scales for the experts + # (param_name, weight_name, expert_id) + ("a13_scale" if weight_name in ["w1", "w3"] else "a2_scale", + f"experts.{expert_id}.{weight_name}.input_scale", expert_id) + for expert_id in range(self.config.num_local_experts) + for weight_name in ["w1", "w2", "w3"] + ] + + params_dict = dict(self.named_parameters()) + for name, loaded_weight in weights: + if "rotary_emb.inv_freq" in name: + continue + + for (param_name, weight_name, shard_id) in stacked_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, loaded_weight, shard_id) + break + else: + for param_name, weight_name, expert_id in expert_params_mapping: + if weight_name not in name: + continue + name = name.replace(weight_name, param_name) + param = params_dict[name] + weight_loader = param.weight_loader + weight_loader(param, + loaded_weight, + weight_name, + expert_id=expert_id) + break + else: + # Skip loading extra bias for GPTQ models. + if name.endswith(".bias") and name not in params_dict: + continue + # Remapping the name of FP8 kv-scale. + if name.endswith("kv_scale"): + remapped_kv_scale_name = name.replace( + ".kv_scale", ".attn.kv_scale") + if remapped_kv_scale_name not in params_dict: + print_warning_once( + "Found kv scale in the checkpoint " + f"(e.g. {name}), but not found the expected " + f"name in the model " + f"(e.g. {remapped_kv_scale_name}). " + "kv-scale is not loaded.") + continue + else: + name = remapped_kv_scale_name + param = params_dict[name] + weight_loader = getattr(param, "weight_loader", + default_weight_loader) + weight_loader(param, loaded_weight) + + +def all_close_1d(x: torch.Tensor) -> bool: + assert len(x.shape) == 1 + return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0])) From 865b7432ab8f405fe1d479445b16c0df7558dfba Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 15:49:10 +0000 Subject: [PATCH 018/114] format --- vllm/model_executor/models/mixtral-old.py | 643 ---------------------- 1 file changed, 643 deletions(-) delete mode 100644 vllm/model_executor/models/mixtral-old.py diff --git a/vllm/model_executor/models/mixtral-old.py b/vllm/model_executor/models/mixtral-old.py deleted file mode 100644 index a662db6d28d00..0000000000000 --- a/vllm/model_executor/models/mixtral-old.py +++ /dev/null @@ -1,643 +0,0 @@ -# coding=utf-8 -# Adapted from -# https://github.com/huggingface/transformers/blob/v4.28.0/src/transformers/models/llama/modeling_llama.py -# Copyright 2023 The vLLM team. -# Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved. -# -# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX -# and OPT implementations in this library. It has been modified from its -# original forms to accommodate minor architectural differences compared -# to GPT-NeoX and OPT used by the Meta AI team that trained the model. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -"""Inference-only Mixtral model.""" -from typing import Iterable, List, Optional, Tuple - -import torch -from torch import nn -from transformers import MixtralConfig - -from vllm import _custom_ops as ops -from vllm.attention import Attention, AttentionMetadata -from vllm.config import CacheConfig, LoRAConfig -from vllm.distributed import (get_tensor_model_parallel_rank, - get_tensor_model_parallel_world_size, - tensor_model_parallel_all_reduce) -from vllm.model_executor.layers.fused_moe import fused_moe -from vllm.model_executor.layers.layernorm import RMSNorm -from vllm.model_executor.layers.linear import (QKVParallelLinear, - ReplicatedLinear, - RowParallelLinear) -from vllm.model_executor.layers.logits_processor import LogitsProcessor -from vllm.model_executor.layers.quantization.base_config import ( - QuantizationConfig) -from vllm.model_executor.layers.quantization.fp8 import (Fp8Config, - per_tensor_dequantize, - per_tensor_quantize) -from vllm.model_executor.layers.rotary_embedding import get_rope -from vllm.model_executor.layers.sampler import Sampler -from vllm.model_executor.layers.vocab_parallel_embedding import ( - DEFAULT_VOCAB_PADDING_SIZE, ParallelLMHead, VocabParallelEmbedding) -from vllm.model_executor.model_loader.weight_utils import default_weight_loader -from vllm.model_executor.sampling_metadata import SamplingMetadata -from vllm.model_executor.utils import set_weight_attrs -from vllm.sequence import SamplerOutput -from vllm.utils import print_warning_once - -from .interfaces import SupportsLoRA - - -class MixtralMoE(nn.Module): - """A tensor-parallel MoE implementation for Mixtral that shards each expert - across all ranks. - - Each expert's weights are sharded across all ranks and a fused MoE - kernel is used for the forward pass, and finally we reduce the outputs - across ranks. - """ - - def __init__( - self, - num_experts: int, - top_k: int, - hidden_size: int, - intermediate_size: int, - params_dtype: Optional[torch.dtype] = None, - tp_size: Optional[int] = None, - quant_config: Optional[QuantizationConfig] = None, - ): - super().__init__() - self.tp_size = tp_size or get_tensor_model_parallel_world_size() - self.num_total_experts = num_experts - self.top_k = top_k - self.hidden_size = hidden_size - self.intermediate_size = intermediate_size // self.tp_size - self.quant_config = quant_config - - # FIXME(pcmoritz): Make this more general to support different - # quantization schemes - self.use_fp8 = isinstance(quant_config, Fp8Config) - - if params_dtype is None: - params_dtype = torch.get_default_dtype() - self.params_dtype = params_dtype - - # Gate always runs at half / full precision for now. - self.gate = ReplicatedLinear(self.hidden_size, - self.num_total_experts, - bias=False, - params_dtype=self.params_dtype, - quant_config=None) - - if self.use_fp8 and self.quant_config.is_checkpoint_fp8_serialized: - params_dtype = torch.float8_e4m3fn - - self.w13_weight = nn.Parameter(torch.empty(self.num_total_experts, - 2 * self.intermediate_size, - self.hidden_size, - dtype=params_dtype), - requires_grad=False) - self.w2_weight = nn.Parameter(torch.empty(self.num_total_experts, - self.hidden_size, - self.intermediate_size, - dtype=params_dtype), - requires_grad=False) - - set_weight_attrs(self.w13_weight, { - "weight_loader": self.weight_loader, - }) - set_weight_attrs(self.w2_weight, { - "weight_loader": self.weight_loader, - }) - - # Used for fp8. - self.w13_scale = None - self.w2_scale = None - self.a13_scale = None - self.a2_scale = None - - if self.use_fp8: - # WEIGHT_SCALE (for fp8) - # Allocate 2 scales for w1 and w3 respectively. - # They will be combined to a single scale after weight loading. - self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts, - 2, - dtype=torch.float32), - requires_grad=False) - self.w2_scale = nn.Parameter(torch.ones(self.num_total_experts, - dtype=torch.float32), - requires_grad=False) - - # If loading fp8 checkpoint, pass the weight loaders. - # If loading an fp16 checkpoint, do not (we will quantize in - # process_weights_after_loading() - if quant_config.is_checkpoint_fp8_serialized: - set_weight_attrs(self.w13_scale, { - "weight_loader": self.weight_loader, - }) - set_weight_attrs(self.w2_scale, { - "weight_loader": self.weight_loader, - }) - - # INPUT_SCALE (for fp8) - if quant_config.activation_scheme == "static": - if not quant_config.is_checkpoint_fp8_serialized: - raise ValueError( - "Found static activation scheme for checkpoint that " - "was not serialized fp8.") - self.a13_scale = nn.Parameter(torch.ones( - self.num_total_experts, dtype=torch.float32), - requires_grad=False) - self.a2_scale = nn.Parameter(torch.ones(self.num_total_experts, - dtype=torch.float32), - requires_grad=False) - - set_weight_attrs(self.a13_scale, { - "weight_loader": self.weight_loader, - }) - set_weight_attrs(self.a2_scale, { - "weight_loader": self.weight_loader, - }) - - def weight_loader(self, param: nn.Parameter, loaded_weight: torch.Tensor, - weight_name: str, expert_id: int): - tp_rank = get_tensor_model_parallel_rank() - param_data = param.data - shard_size = self.intermediate_size - shard = slice(tp_rank * shard_size, (tp_rank + 1) * shard_size) - if weight_name.endswith("w1.weight"): - param_data[expert_id, 0:shard_size, :] = loaded_weight[shard, :] - if weight_name.endswith("w3.weight"): - param_data[expert_id, - shard_size:2 * shard_size, :] = loaded_weight[shard, :] - if weight_name.endswith("w2.weight"): - param_data[expert_id, :, :] = loaded_weight[:, shard] - - # Loading scales - if "input_scale" in weight_name or "w2.weight_scale" in weight_name: - if param_data[expert_id] != 1 and (param_data[expert_id] - - loaded_weight).abs() > 1e-5: - raise ValueError( - "input_scales of w1 and w3 of a layer " - f"must be equal. But got {param_data[expert_id]} " - f"vs. {loaded_weight}") - param_data[expert_id] = loaded_weight - elif "weight_scale" in weight_name: - # We have to keep the weight scales of w1 and w3 because - # we need to re-quantize w1/w3 weights after weight loading. - assert "w1" in weight_name or "w3" in weight_name - shard_id = 0 if "w1" in weight_name else 1 - param_data[expert_id][shard_id] = loaded_weight - - def process_weights_after_loading(self): - # Fp8 is the only case where we need to process after loading. - if not self.use_fp8: - return - - # If checkpoint is fp16, quantize here. - if not self.quant_config.is_checkpoint_fp8_serialized: - w13_weight = torch.empty_like(self.w13_weight.data, - dtype=torch.float8_e4m3fn) - w2_weight = torch.empty_like(self.w2_weight.data, - dtype=torch.float8_e4m3fn) - - # Re-initialize w13_scale because we directly quantize - # merged w13 weights and generate a single scaling factor. - self.w13_scale = nn.Parameter(torch.ones(self.num_total_experts, - dtype=torch.float32), - requires_grad=False) - for expert in range(self.num_total_experts): - w13_weight[expert, :, :], self.w13_scale[ - expert] = ops.scaled_fp8_quant( - self.w13_weight.data[expert, :, :]) - w2_weight[expert, :, :], self.w2_scale[ - expert] = ops.scaled_fp8_quant( - self.w2_weight.data[expert, :, :]) - self.w13_weight = nn.Parameter(w13_weight, requires_grad=False) - self.w2_weight = nn.Parameter(w2_weight, requires_grad=False) - - else: - # If checkpoint is fp8 + static, cleanup input_scales. - # Since state_dict has an input_scale per expert but our kernels - # are passed one input_scale shared across all experts. - if self.quant_config.activation_scheme == "static": - if self.a13_scale is None or self.a2_scale is None: - raise ValueError( - "QuantConfig has static quantization, but found " - "activation scales are None.") - - if (not all_close_1d(self.a13_scale) - or not all_close_1d(self.a2_scale)): - print_warning_once( - "Found input_scales that are not equal for " - "fp8 MoE layer. Using the maximum across experts " - "for each layer. ") - - self.a13_scale = nn.Parameter(self.a13_scale.max(), - requires_grad=False) - self.a2_scale = nn.Parameter(self.a2_scale.max(), - requires_grad=False) - - assert self.w13_scale is not None - shard_size = self.intermediate_size - max_w13_scales = self.w13_scale.max(dim=1).values - for expert_id in range(self.num_total_experts): - start = 0 - for shard_id in range(2): - dq_weight = per_tensor_dequantize( - self.w13_weight[expert_id][start:start + - shard_size, :], - self.w13_scale[expert_id][shard_id]) - self.w13_weight[expert_id][ - start:start + shard_size, :] = per_tensor_quantize( - dq_weight, max_w13_scales[expert_id]) - start += shard_size - - self.w13_scale = nn.Parameter(max_w13_scales, requires_grad=False) - - def forward(self, hidden_states: torch.Tensor) -> torch.Tensor: - num_tokens, hidden_size = hidden_states.shape - hidden_states = hidden_states.view(-1, self.hidden_size) - # router_logits: (num_tokens, n_experts) - router_logits, _ = self.gate(hidden_states) - final_hidden_states = fused_moe(hidden_states, - self.w13_weight, - self.w2_weight, - router_logits, - self.top_k, - renormalize=True, - inplace=True, - use_fp8=self.use_fp8, - w1_scale=self.w13_scale, - w2_scale=self.w2_scale, - a1_scale=self.a13_scale, - a2_scale=self.a2_scale) - - if self.tp_size > 1: - final_hidden_states = tensor_model_parallel_all_reduce( - final_hidden_states) - - return final_hidden_states.view(num_tokens, hidden_size) - - -class MixtralAttention(nn.Module): - - def __init__( - self, - hidden_size: int, - num_heads: int, - num_kv_heads: int, - max_position: int = 4096 * 32, - rope_theta: float = 10000, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - ) -> None: - super().__init__() - self.hidden_size = hidden_size - tp_size = get_tensor_model_parallel_world_size() - self.total_num_heads = num_heads - assert self.total_num_heads % tp_size == 0 - self.num_heads = self.total_num_heads // tp_size - self.total_num_kv_heads = num_kv_heads - if self.total_num_kv_heads >= tp_size: - # Number of KV heads is greater than TP size, so we partition - # the KV heads across multiple tensor parallel GPUs. - assert self.total_num_kv_heads % tp_size == 0 - else: - # Number of KV heads is less than TP size, so we replicate - # the KV heads across multiple tensor parallel GPUs. - assert tp_size % self.total_num_kv_heads == 0 - self.num_kv_heads = max(1, self.total_num_kv_heads // tp_size) - self.head_dim = hidden_size // self.total_num_heads - self.q_size = self.num_heads * self.head_dim - self.kv_size = self.num_kv_heads * self.head_dim - self.scaling = self.head_dim**-0.5 - self.rope_theta = rope_theta - - self.qkv_proj = QKVParallelLinear( - hidden_size, - self.head_dim, - self.total_num_heads, - self.total_num_kv_heads, - bias=False, - quant_config=quant_config, - ) - self.o_proj = RowParallelLinear( - self.total_num_heads * self.head_dim, - hidden_size, - bias=False, - quant_config=quant_config, - ) - self.rotary_emb = get_rope( - self.head_dim, - rotary_dim=self.head_dim, - max_position=max_position, - base=int(self.rope_theta), - is_neox_style=True, - ) - self.attn = Attention(self.num_heads, - self.head_dim, - self.scaling, - num_kv_heads=self.num_kv_heads, - cache_config=cache_config, - quant_config=quant_config) - - def forward( - self, - positions: torch.Tensor, - hidden_states: torch.Tensor, - kv_cache: torch.Tensor, - attn_metadata: AttentionMetadata, - ) -> torch.Tensor: - qkv, _ = self.qkv_proj(hidden_states) - q, k, v = qkv.split([self.q_size, self.kv_size, self.kv_size], dim=-1) - q, k = self.rotary_emb(positions, q, k) - attn_output = self.attn(q, k, v, kv_cache, attn_metadata) - output, _ = self.o_proj(attn_output) - return output - - -class MixtralDecoderLayer(nn.Module): - - def __init__( - self, - config: MixtralConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - ) -> None: - super().__init__() - self.hidden_size = config.hidden_size - # Requires transformers > 4.32.0 - rope_theta = getattr(config, "rope_theta", 10000) - self.self_attn = MixtralAttention( - hidden_size=self.hidden_size, - num_heads=config.num_attention_heads, - max_position=config.max_position_embeddings, - num_kv_heads=config.num_key_value_heads, - rope_theta=rope_theta, - cache_config=cache_config, - quant_config=quant_config) - self.block_sparse_moe = MixtralMoE( - num_experts=config.num_local_experts, - top_k=config.num_experts_per_tok, - hidden_size=config.hidden_size, - intermediate_size=config.intermediate_size, - quant_config=quant_config) - self.input_layernorm = RMSNorm(config.hidden_size, - eps=config.rms_norm_eps) - self.post_attention_layernorm = RMSNorm(config.hidden_size, - eps=config.rms_norm_eps) - - def forward( - self, - positions: torch.Tensor, - hidden_states: torch.Tensor, - kv_cache: torch.Tensor, - attn_metadata: AttentionMetadata, - residual: Optional[torch.Tensor], - ) -> torch.Tensor: - # Self Attention - if residual is None: - residual = hidden_states - hidden_states = self.input_layernorm(hidden_states) - else: - hidden_states, residual = self.input_layernorm( - hidden_states, residual) - hidden_states = self.self_attn( - positions=positions, - hidden_states=hidden_states, - kv_cache=kv_cache, - attn_metadata=attn_metadata, - ) - - # Fully Connected - hidden_states, residual = self.post_attention_layernorm( - hidden_states, residual) - hidden_states = self.block_sparse_moe(hidden_states) - return hidden_states, residual - - -class MixtralModel(nn.Module): - - def __init__( - self, - config: MixtralConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - lora_config: Optional[LoRAConfig] = None, - ) -> None: - super().__init__() - self.padding_idx = config.pad_token_id - lora_vocab = (lora_config.lora_extra_vocab_size * - (lora_config.max_loras or 1)) if lora_config else 0 - self.vocab_size = config.vocab_size + lora_vocab - self.org_vocab_size = config.vocab_size - - self.embed_tokens = VocabParallelEmbedding( - self.vocab_size, - config.hidden_size, - org_num_embeddings=config.vocab_size, - ) - self.layers = nn.ModuleList([ - MixtralDecoderLayer(config, - cache_config, - quant_config=quant_config) - for _ in range(config.num_hidden_layers) - ]) - self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps) - - def forward( - self, - input_ids: torch.Tensor, - positions: torch.Tensor, - kv_caches: List[torch.Tensor], - attn_metadata: AttentionMetadata, - ) -> torch.Tensor: - hidden_states = self.embed_tokens(input_ids) - residual = None - for i in range(len(self.layers)): - layer = self.layers[i] - hidden_states, residual = layer(positions, hidden_states, - kv_caches[i], attn_metadata, - residual) - hidden_states, _ = self.norm(hidden_states, residual) - return hidden_states - - -class MixtralForCausalLM(nn.Module, SupportsLoRA): - fall_back_to_pt_during_load = False - - packed_modules_mapping = { - "qkv_proj": [ - "q_proj", - "k_proj", - "v_proj", - ], - } - - # LoRA specific attributes - supported_lora_modules = [ - "qkv_proj", - "o_proj", - "embed_tokens", - "lm_head", - ] - embedding_modules = { - "embed_tokens": "input_embeddings", - "lm_head": "output_embeddings", - } - embedding_padding_modules = ["lm_head"] - - def __init__( - self, - config: MixtralConfig, - cache_config: Optional[CacheConfig] = None, - quant_config: Optional[QuantizationConfig] = None, - lora_config: Optional[LoRAConfig] = None, - ) -> None: - super().__init__() - - self.config = config - self.lora_config = lora_config - - self.model = MixtralModel(config, - cache_config, - quant_config, - lora_config=lora_config) - self.unpadded_vocab_size = config.vocab_size - if lora_config: - self.unpadded_vocab_size += lora_config.lora_extra_vocab_size - self.lm_head = ParallelLMHead( - self.unpadded_vocab_size, - config.hidden_size, - org_num_embeddings=config.vocab_size, - padding_size=DEFAULT_VOCAB_PADDING_SIZE - # We need bigger padding if using lora for kernel - # compatibility - if not lora_config else lora_config.lora_vocab_padding_size, - ) - self.logits_processor = LogitsProcessor(self.unpadded_vocab_size, - config.vocab_size) - self.sampler = Sampler() - - def forward( - self, - input_ids: torch.Tensor, - positions: torch.Tensor, - kv_caches: List[torch.Tensor], - attn_metadata: AttentionMetadata, - ) -> torch.Tensor: - hidden_states = self.model(input_ids, positions, kv_caches, - attn_metadata) - return hidden_states - - def compute_logits(self, hidden_states: torch.Tensor, - sampling_metadata: SamplingMetadata) -> torch.Tensor: - logits = self.logits_processor(self.lm_head.weight, hidden_states, - sampling_metadata) - return logits - - def sample( - self, - logits: Optional[torch.Tensor], - sampling_metadata: SamplingMetadata, - ) -> Optional[SamplerOutput]: - next_tokens = self.sampler(logits, sampling_metadata) - return next_tokens - - def load_weights(self, weights: Iterable[Tuple[str, torch.Tensor]]): - stacked_params_mapping = [ - # (param_name, shard_name, shard_id) - ("qkv_proj", "q_proj", "q"), - ("qkv_proj", "k_proj", "k"), - ("qkv_proj", "v_proj", "v"), - ] - - expert_params_mapping = [ - # These are the weight scales for the experts - # (param_name, weight_name, expert_id) - ("w13_scale" if weight_name in ["w1", "w3"] else "w2_scale", - f"experts.{expert_id}.{weight_name}.weight_scale", expert_id) - for expert_id in range(self.config.num_local_experts) - for weight_name in ["w1", "w2", "w3"] - ] + [ - # These are the weights for the experts - # (param_name, weight_name, expert_id) - ("w13_weight" if weight_name in ["w1", "w3"] else "w2_weight", - f"experts.{expert_id}.{weight_name}.weight", expert_id) - for expert_id in range(self.config.num_local_experts) - for weight_name in ["w1", "w2", "w3"] - ] + [ - # These are the activation scales for the experts - # (param_name, weight_name, expert_id) - ("a13_scale" if weight_name in ["w1", "w3"] else "a2_scale", - f"experts.{expert_id}.{weight_name}.input_scale", expert_id) - for expert_id in range(self.config.num_local_experts) - for weight_name in ["w1", "w2", "w3"] - ] - - params_dict = dict(self.named_parameters()) - for name, loaded_weight in weights: - if "rotary_emb.inv_freq" in name: - continue - - for (param_name, weight_name, shard_id) in stacked_params_mapping: - if weight_name not in name: - continue - name = name.replace(weight_name, param_name) - # Skip loading extra bias for GPTQ models. - if name.endswith(".bias") and name not in params_dict: - continue - param = params_dict[name] - weight_loader = param.weight_loader - weight_loader(param, loaded_weight, shard_id) - break - else: - for param_name, weight_name, expert_id in expert_params_mapping: - if weight_name not in name: - continue - name = name.replace(weight_name, param_name) - param = params_dict[name] - weight_loader = param.weight_loader - weight_loader(param, - loaded_weight, - weight_name, - expert_id=expert_id) - break - else: - # Skip loading extra bias for GPTQ models. - if name.endswith(".bias") and name not in params_dict: - continue - # Remapping the name of FP8 kv-scale. - if name.endswith("kv_scale"): - remapped_kv_scale_name = name.replace( - ".kv_scale", ".attn.kv_scale") - if remapped_kv_scale_name not in params_dict: - print_warning_once( - "Found kv scale in the checkpoint " - f"(e.g. {name}), but not found the expected " - f"name in the model " - f"(e.g. {remapped_kv_scale_name}). " - "kv-scale is not loaded.") - continue - else: - name = remapped_kv_scale_name - param = params_dict[name] - weight_loader = getattr(param, "weight_loader", - default_weight_loader) - weight_loader(param, loaded_weight) - - -def all_close_1d(x: torch.Tensor) -> bool: - assert len(x.shape) == 1 - return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0])) From 9c245259024504365fecbfb128f7ba2de21a4bf4 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 15:49:40 +0000 Subject: [PATCH 019/114] newline --- vllm/model_executor/layers/quantization/utils/fp8_utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py index 36337dca2cc63..87bf80ff274cf 100644 --- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py +++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py @@ -106,4 +106,3 @@ def fp8_apply( bias=bias) return torch.narrow(output, 0, 0, input.shape[0]) - \ No newline at end of file From 8b5ac5a46c99546a4b8303b4304754d3a6d51bc4 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 16:34:38 +0000 Subject: [PATCH 020/114] formatting --- .../compressed_tensors/compressed_tensors.py | 53 +++++++++++++++---- .../compressed_tensors/schemes/__init__.py | 31 +++++++---- .../schemes/compressed_tensors_scheme.py | 1 + .../schemes/compressed_tensors_w8a8.py | 9 ++-- .../schemes/compressed_tensors_w8a8_fp8.py | 36 ++++++------- .../quantization/compressed_tensors/utils.py | 1 + .../model_executor/layers/quantization/fp8.py | 16 +++--- .../layers/quantization/utils/fp8_utils.py | 32 ++++++----- 8 files changed, 115 insertions(+), 64 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 3f0e95852fa4c..ec3c4988a8e84 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -7,10 +7,10 @@ from vllm.model_executor.layers.quantization.base_config import ( # noqa: E501 QuantizationConfig) from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( - W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensorsFp8, + W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensorsScheme, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8DynamicToken, CompressedTensorsW8A8StaticTensor, - CompressedTensorsWNA16) + CompressedTensorsW8A8DynamicToken, CompressedTensorsW8A8Fp8, + CompressedTensorsW8A8StaticTensor, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( CompressionFormat, QuantizationArgs, QuantizationStrategy, QuantizationType, find_first_name_or_class_match) @@ -118,6 +118,40 @@ def _is_dynamic_token_w8a8(self, weight_quant: BaseModel, return is_8_bits and is_token and is_symmetric and is_dynamic + def _is_fp8_w8a8(self, weight_quant: BaseModel, + input_quant: BaseModel) -> bool: + # Confirm weights and activations quantized. + if weight_quant is None or input_quant is None: + return False + + # Confirm we have floating points. + if not (weight_quant.type == QuantizationType.FLOAT + and input_quant.type == QuantizationType.FLOAT): + return False + + # Confirm weight scheme is supported. + is_symmetric_weight = weight_quant.symmetric + is_static_weight = not weight_quant.dynamic + is_per_tensor_weight = ( + weight_quant.strategy == QuantizationStrategy.TENSOR) + if not (is_symmetric_weight and is_static_weight + and is_per_tensor_weight): + return False + + # Dynamic quantization is always supported if weights supported. + if input_quant.dynamic: + return True + + # Confirm activation scheme is supported. + is_symmetric_activation = input_quant.symmetric + is_per_tensor_activation = ( + input_quant.strategy == QuantizationStrategy.TENSOR) + if not (is_symmetric_activation and is_per_tensor_activation): + return False + + # All conditions satisfied. + return True + def _is_wNa16_group_channel(self, weight_quant: BaseModel, input_quant: BaseModel) -> bool: input_quant_none = input_quant is None @@ -130,9 +164,6 @@ def _is_wNa16_group_channel(self, weight_quant: BaseModel, return (is_channel_group and input_quant_none and is_symmetric and is_static) - def _is_fp8(self, weight_quant: BaseModel, input_quant: BaseModel) -> bool: - return weight_quant.type == QuantizationType.FLOAT - def _get_schema(self, weight_quant: BaseModel, input_quant: BaseModel) -> "CompressedTensorsScheme": @@ -151,9 +182,11 @@ def _get_schema(self, weight_quant: BaseModel, strategy=weight_quant.strategy, group_size=weight_quant.group_size) - if self.quant_format == CompressionFormat.int_quantized.value: - if self._is_fp8(weight_quant, input_quant): - return CompressedTensorsFp8(input_dynamic=input_quant.dynamic) + if (self.quant_format == CompressionFormat.int_quantized + or self.quant_format == CompressionFormat.float_quantized): + if self._is_fp8_w8a8(weight_quant, input_quant): + return CompressedTensorsW8A8Fp8( + input_dynamic=input_quant.dynamic) if self._is_static_tensor_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8StaticTensor( @@ -194,7 +227,7 @@ def __init__(self, quantization_config: CompressedTensorsConfig): self.quantization_config = quantization_config def process_weights_after_loading(self, layer: torch.nn.Module) -> None: - layer.scheme.process_weights_after_loading(layer) + layer.scheme.process_weights_after_loading(layer) def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index 271482847d260..9fbc725543042 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -1,12 +1,23 @@ -from .compressed_tensors_scheme import CompressedTensorsScheme # noqa: F401 -from .compressed_tensors_unquantized import ( # noqa: F401 - CompressedTensorsUnquantized) -from .compressed_tensors_w4a16_24 import ( # noqa: F401 - W4A16SPARSE24_SUPPORTED_BITS, CompressedTensorsW4A16Sparse24) -from .compressed_tensors_w8a8_dynamictoken import ( # noqa: F401, E501 +from .compressed_tensors_scheme import CompressedTensorsScheme +from .compressed_tensors_unquantized import CompressedTensorsUnquantized +from .compressed_tensors_w4a16_24 import (W4A16SPARSE24_SUPPORTED_BITS, + CompressedTensorsW4A16Sparse24) +from .compressed_tensors_w8a8_dynamictoken import ( CompressedTensorsW8A8DynamicToken) -from .compressed_tensors_w8a8_fp8 import CompressedTensorsFp8 # noqa: F401 -from .compressed_tensors_w8a8_statictensor import ( # noqa: F401, E501 +from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8 +from .compressed_tensors_w8a8_statictensor import ( CompressedTensorsW8A8StaticTensor) -from .compressed_tensors_wNa16 import WNA16_SUPPORTED_BITS # noqa: F401 -from .compressed_tensors_wNa16 import CompressedTensorsWNA16 # noqa: F401 +from .compressed_tensors_wNa16 import (WNA16_SUPPORTED_BITS, + CompressedTensorsWNA16) + +__all__ = [ + "CompressedTensorsScheme", + "CompressedTensorsUnquantized", + "CompressedTensorsWNA16", + "CompressedTensorsW4A16Sparse24", + "CompressedTensorsW8A8DynamicToken", + "CompressedTensorsW8A8StaticTensor", + "CompressedTensorsW8A8Fp8", + "WNA16_SUPPORTED_BITS", + "W4A16SPARSE24_SUPPORTED_BITS", +] diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py index bd628f770fa00..a480f7ca8386c 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py @@ -32,6 +32,7 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): """ raise NotImplementedError + @abstractmethod def process_weights_after_loading(self, layer: torch.nn.Module): """ Called after weight loading is complete for any cleanup that diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 79c9574bde97a..3172a9dba3ab6 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -10,20 +10,17 @@ from vllm.model_executor.utils import set_weight_attrs - class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, - strategy: QuantizationStrategy): + def __init__(self, strategy: QuantizationStrategy): self.strategy = strategy - # If fused module, we have N scales for N fused weights, but Cutlass kernels + # If fused module, we have N scales for N fused weights, but Cutlass kernels # support only per-tensor and per-channel cases. So we need to handle this # by converting the the N per-tensor scales to channelwise. def process_weights_after_loading(self, layer: torch.nn.Module) -> None: is_fused_module = len(self.logical_widths) > 1 - if (self.strategy == QuantizationStrategy.TENSOR - and is_fused_module): + if (self.strategy == QuantizationStrategy.TENSOR and is_fused_module): # Load the N per-tensor scales into the channelwise buffer. weight_scale_channel = torch.empty( diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index dd60b249ee58d..bcd3d34a90314 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -5,26 +5,25 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - fp8_apply, requantize_with_max_scale, create_scale_param, - cutlass_fp8_supported) + create_scale_param, cutlass_fp8_supported, fp8_apply, + requantize_with_max_scale) from vllm.model_executor.utils import set_weight_attrs -__all__ = ["CompressedTensorsW8A8FP8"] +__all__ = ["CompressedTensorsW8A8Fp8"] -class CompressedTensorsFp8(CompressedTensorsScheme): +class CompressedTensorsW8A8Fp8(CompressedTensorsScheme): + def __init__(self, input_dynamic: bool): self.input_dynamic = input_dynamic self.cutlass_fp8_supported = cutlass_fp8_supported() - - def create_weights(self, - layer: torch.nn.Module, + + def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], input_size_per_partition: int, - params_dtype: torch.dtype, - weight_loader: Callable, + params_dtype: torch.dtype, weight_loader: Callable, **kwargs): - + del params_dtype output_size_per_partition = sum(output_partition_sizes) @@ -37,7 +36,8 @@ def create_weights(self, requires_grad=False) layer.register_parameter("weight", weight) set_weight_attrs(weight, { - "input_dim": 1, "output_dim": 0, + "input_dim": 1, + "output_dim": 0, "weight_loader": weight_loader, }) @@ -45,16 +45,15 @@ def create_weights(self, weight_scale = create_scale_param(output_partition_sizes, weight_loader=weight_loader) layer.register_parameter("weight_scale", weight_scale) - + # INPUT SCALE if not self.input_dynamic: input_scale = create_scale_param(output_partition_sizes, weight_loader=weight_loader) - layer.register_parameter("input_scale", input_scale) - + layer.register_parameter("input_scale", input_scale) def process_weights_after_loading(self, layer) -> None: - # Dequant -> Quant with max scale. + # Dequant -> Quant with max scale. max_w_scale, weight = requantize_with_max_scale( weight=layer.weight, weight_scale=layer.weight_scale, @@ -63,10 +62,11 @@ def process_weights_after_loading(self, layer) -> None: # Update layer with new values. layer.weight = torch.nn.Parameter(weight.t(), requires_grad=False) - layer.weight_scale = torch.nn.Parameter(max_w_scale, requires_grad=False) + layer.weight_scale = torch.nn.Parameter(max_w_scale, + requires_grad=False) if self.input_dynamic: layer.input_scale = None - else: + else: layer.input_scale = torch.nn.Parameter(layer.input_scale.max(), requires_grad=False) @@ -74,7 +74,7 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - + return fp8_apply(input=x, weight=layer.weight, weight_scale=layer.weight_scale, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/utils.py b/vllm/model_executor/layers/quantization/compressed_tensors/utils.py index b2bec9b603d1a..5b44c215535b5 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/utils.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/utils.py @@ -9,6 +9,7 @@ class CompressionFormat(Enum): dense = "dense" sparse_bitmask = "sparse-bitmask" + float_quantized = "float-quantized" int_quantized = "int-quantized" pack_quantized = "pack-quantized" marlin_24 = "marlin-24" diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index 6ef29afa9d6ec..1a5bededb085e 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -1,4 +1,4 @@ -from typing import Any, Dict, List, Optional, Union +from typing import Any, Dict, List, Optional import torch from torch.nn import Module @@ -9,12 +9,11 @@ from vllm.model_executor.layers.fused_moe import (FusedMoE, FusedMoEMethodBase, fused_moe) from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase -from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - fp8_apply, requantize_with_max_scale, all_close_1d, - per_tensor_dequantize, per_tensor_quantize, create_scale_param, - cutlass_fp8_supported) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) +from vllm.model_executor.layers.quantization.utils.fp8_utils import ( + all_close_1d, create_scale_param, cutlass_fp8_supported, fp8_apply, + per_tensor_dequantize, per_tensor_quantize, requantize_with_max_scale) from vllm.model_executor.utils import set_weight_attrs from vllm.utils import print_warning_once @@ -22,6 +21,7 @@ logger = init_logger(__name__) + class Fp8Config(QuantizationConfig): """Config class for FP8.""" @@ -142,7 +142,7 @@ def create_weights( # INPUT ACTIVATION SCALE if self.quant_config.activation_scheme == "static": scale = create_scale_param(output_partition_sizes, - **extra_weight_attrs) + **extra_weight_attrs) layer.register_parameter("input_scale", scale) def process_weights_after_loading(self, layer: Module) -> None: @@ -151,7 +151,7 @@ def process_weights_after_loading(self, layer: Module) -> None: if not self.quant_config.is_checkpoint_fp8_serialized: qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, scale=None) - + # Update the layer with the new values. layer.weight = Parameter(qweight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) @@ -161,7 +161,7 @@ def process_weights_after_loading(self, layer: Module) -> None: # If checkpoint is fp8, requantize the separately quantized logical # weights into a single fp8 weight with a single weight scale. else: - # Dequant -> Quant with max scale. + # Dequant -> Quant with max scale. max_w_scale, weight = requantize_with_max_scale( weight=layer.weight, weight_scale=layer.weight_scale, diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py index 87bf80ff274cf..81dec7ffab5bb 100644 --- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py +++ b/vllm/model_executor/layers/quantization/utils/fp8_utils.py @@ -1,9 +1,11 @@ +from typing import List, Optional, Tuple, Union + import torch -from typing import Optional, List, Tuple, Union from vllm import _custom_ops as ops -from vllm.platforms import current_platform from vllm.model_executor.utils import set_weight_attrs +from vllm.platforms import current_platform + def cutlass_fp8_supported() -> bool: capability = current_platform.get_device_capability() @@ -11,12 +13,14 @@ def cutlass_fp8_supported() -> bool: return ops.cutlass_scaled_mm_supports_fp8(capability) + def per_tensor_quantize(tensor: torch.Tensor, inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: finfo = torch.finfo(torch.float8_e4m3fn) qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) return qweight.to(torch.float8_e4m3fn) + def per_tensor_dequantize( tensor: torch.Tensor, inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: @@ -24,15 +28,17 @@ def per_tensor_dequantize( dq_weight = fake_qweight * inv_scale return dq_weight + def all_close_1d(x: torch.Tensor) -> bool: assert len(x.shape) == 1 return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0])) + def create_scale_param(output_partition_sizes: List[int], **extra_weight_attrs) -> torch.nn.Parameter: scale = torch.nn.Parameter(torch.empty(len(output_partition_sizes), dtype=torch.float32), - requires_grad=False) + requires_grad=False) scale[:] = torch.finfo(torch.float8_e4m3fn).min set_weight_attrs(scale, { **extra_weight_attrs, @@ -40,11 +46,10 @@ def create_scale_param(output_partition_sizes: List[int], }) return scale + def requantize_with_max_scale( - weight: torch.Tensor, - weight_scale: torch.Tensor, - logical_widths: List[int] -) -> Tuple[torch.Tensor, torch.Tensor]: + weight: torch.Tensor, weight_scale: torch.Tensor, + logical_widths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]: # Max scale to be used for requanitzation. max_w_scale = weight_scale.max() @@ -54,20 +59,22 @@ def requantize_with_max_scale( # from disk in this case. Skip requantization in this case (since) # we already are quantized with the single scale. # * Sample Model: nm-testing/Phi-3-mini-128k-instruct-FP8 - unfused_module_in_checkpoint = ( - weight_scale[-1] > torch.finfo(torch.float8_e4m3fn).min) - + unfused_module_in_checkpoint = (weight_scale[-1] > torch.finfo( + torch.float8_e4m3fn).min) + # If unfused checkpoint, need requanize with the single scale. if unfused_module_in_checkpoint: start = 0 for idx, logical_width in enumerate(logical_widths): end = start + logical_width - weight_dq = per_tensor_dequantize(weight[start:end, :], weight_scale[idx]) + weight_dq = per_tensor_dequantize(weight[start:end, :], + weight_scale[idx]) weight[start:end, :] = per_tensor_quantize(weight_dq, max_w_scale) start = end return max_w_scale, weight + def fp8_apply( input: torch.Tensor, weight: torch.Tensor, @@ -91,7 +98,8 @@ def fp8_apply( scale_b=weight_scale) else: - qinput, x_scale = ops.scaled_fp8_quant(input, input_scale, + qinput, x_scale = ops.scaled_fp8_quant(input, + input_scale, batch_dim_padding=17) # Fused GEMM_DQ -- note we padded the input above because From 0e46e4b73823675330281d07f31227aaf6f8b879 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 16:50:45 +0000 Subject: [PATCH 021/114] working --- .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh | 2 +- .buildkite/lm-eval-harness/test_lm_eval_correctness.py | 3 ++- .../quantization/compressed_tensors/compressed_tensors.py | 4 ++-- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh b/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh index a2876bade8893..933733e9c1edf 100644 --- a/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh +++ b/.buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh @@ -46,6 +46,6 @@ while getopts "m:b:l:f:t:" OPT; do done lm_eval --model vllm \ - --model_args pretrained=$MODEL,tensor_parallel_size=$TP_SIZE \ + --model_args pretrained=$MODEL,tensor_parallel_size=$TP_SIZE,add_bos_token=true \ --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \ --batch_size $BATCH_SIZE diff --git a/.buildkite/lm-eval-harness/test_lm_eval_correctness.py b/.buildkite/lm-eval-harness/test_lm_eval_correctness.py index 975841dad1c29..7fdce7b53bd7f 100644 --- a/.buildkite/lm-eval-harness/test_lm_eval_correctness.py +++ b/.buildkite/lm-eval-harness/test_lm_eval_correctness.py @@ -24,7 +24,8 @@ def launch_lm_eval(eval_config): model_args = f"pretrained={eval_config['model_name']}," \ - f"tensor_parallel_size={TP_SIZE}" + f"tensor_parallel_size={TP_SIZE}," \ + f"add_bos_token=true" results = lm_eval.simple_evaluate( model="vllm", diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index ec3c4988a8e84..4500e5388c1d0 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -182,8 +182,8 @@ def _get_schema(self, weight_quant: BaseModel, strategy=weight_quant.strategy, group_size=weight_quant.group_size) - if (self.quant_format == CompressionFormat.int_quantized - or self.quant_format == CompressionFormat.float_quantized): + if (self.quant_format == CompressionFormat.int_quantized.value or + self.quant_format == CompressionFormat.float_quantized.value): if self._is_fp8_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8Fp8( input_dynamic=input_quant.dynamic) From a47a2513105faff6c9ea0bb77d1f3dbe00b75bc9 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 16:57:19 +0000 Subject: [PATCH 022/114] added compressed tensors fp8 to automation --- .../configs/Meta-Llama-3-8B-Instruct-FP8.yaml | 8 ++++---- .buildkite/lm-eval-harness/configs/models-small.txt | 1 + 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml index 02668702b83af..04be8bc8797df 100644 --- a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml +++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml @@ -1,11 +1,11 @@ -# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1 -model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8" +# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 250 -f 5 -t 1 +model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test" tasks: - name: "gsm8k" metrics: - name: "exact_match,strict-match" - value: 0.756 + value: 0.776 - name: "exact_match,flexible-extract" - value: 0.752 + value: 0.776 limit: 250 num_fewshot: 5 diff --git a/.buildkite/lm-eval-harness/configs/models-small.txt b/.buildkite/lm-eval-harness/configs/models-small.txt index 273c5482db264..fc3f0144f8a04 100644 --- a/.buildkite/lm-eval-harness/configs/models-small.txt +++ b/.buildkite/lm-eval-harness/configs/models-small.txt @@ -1,2 +1,3 @@ Meta-Llama-3-8B-Instruct.yaml Meta-Llama-3-8B-Instruct-FP8.yaml +Meta-Llama-3-8B-FP8-compressed-tensors.yaml From c6be5360e14b583ee76f40c0c140d96edae7aa4d Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 16:58:13 +0000 Subject: [PATCH 023/114] missed file --- ...ta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml | 11 +++++++++++ .../configs/Meta-Llama-3-8B-Instruct-FP8.yaml | 8 ++++---- 2 files changed, 15 insertions(+), 4 deletions(-) create mode 100644 .buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml diff --git a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml new file mode 100644 index 0000000000000..3aef36c8e98dd --- /dev/null +++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8-compressed-tensors.yaml @@ -0,0 +1,11 @@ +# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 250 -f 5 -t 1 +model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test" +tasks: +- name: "gsm8k" + metrics: + - name: "exact_match,strict-match" + value: 0.776 + - name: "exact_match,flexible-extract" + value: 0.776 +limit: 250 +num_fewshot: 5 diff --git a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml index 04be8bc8797df..7a89e8e0c76f2 100644 --- a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml +++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-FP8.yaml @@ -1,11 +1,11 @@ -# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-hf-baseline.sh -m nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test -b 32 -l 250 -f 5 -t 1 -model_name: "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test" +# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m neuralmagic/Meta-Llama-3-8B-Instruct-FP8 -b 32 -l 250 -f 5 -t 1 +model_name: "neuralmagic/Meta-Llama-3-8B-Instruct-FP8" tasks: - name: "gsm8k" metrics: - name: "exact_match,strict-match" - value: 0.776 + value: 0.756 - name: "exact_match,flexible-extract" - value: 0.776 + value: 0.752 limit: 250 num_fewshot: 5 From 04411713c72f529390a2dd171dc190dd6075fa05 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 17:02:31 +0000 Subject: [PATCH 024/114] format --- .../quantization/compressed_tensors/compressed_tensors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 4500e5388c1d0..dad1321035f49 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -183,7 +183,7 @@ def _get_schema(self, weight_quant: BaseModel, group_size=weight_quant.group_size) if (self.quant_format == CompressionFormat.int_quantized.value or - self.quant_format == CompressionFormat.float_quantized.value): + self.quant_format == CompressionFormat.float_quantized.value): if self._is_fp8_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8Fp8( input_dynamic=input_quant.dynamic) From d404f00c21c6987b22a431c555543765b67cd95f Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 17:05:11 +0000 Subject: [PATCH 025/114] remove unnecessary file changes --- .../schemes/compressed_tensors_w8a8.py | 13 ++++++------- 1 file changed, 6 insertions(+), 7 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 3172a9dba3ab6..49779057659f0 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -12,15 +12,15 @@ class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, strategy: QuantizationStrategy): + def __init__(self, strategy: str): self.strategy = strategy - # If fused module, we have N scales for N fused weights, but Cutlass kernels - # support only per-tensor and per-channel cases. So we need to handle this - # by converting the the N per-tensor scales to channelwise. + # Cutlass kernels support only per-tensor and per-channel cases. + # So if we have a fused module (QKV, MLP) with per tensor scales (thus N + # scales being passed to the kernel), we convert to the per-channel case. def process_weights_after_loading(self, layer: torch.nn.Module) -> None: - is_fused_module = len(self.logical_widths) > 1 - if (self.strategy == QuantizationStrategy.TENSOR and is_fused_module): + if (self.strategy == QuantizationStrategy.TENSOR + and len(self.logical_widths) > 1): # Load the N per-tensor scales into the channelwise buffer. weight_scale_channel = torch.empty( @@ -41,7 +41,6 @@ def create_weights(self, layer: torch.nn.Module, input_size_per_partition: int, params_dtype: torch.dtype, weight_loader: Callable, **kwargs): - self.logical_widths = output_partition_sizes # WEIGHT SCALE From 6569323b1a366e1473859f63e22544e7798c79e7 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 18:10:54 +0000 Subject: [PATCH 026/114] restructure quant ops --- .../compressed_tensors/compressed_tensors.py | 11 ++--- .../compressed_tensors/schemes/__init__.py | 6 +-- .../schemes/compressed_tensors_w8a8.py | 30 +++++++++++- .../compressed_tensors_w8a8_dynamictoken.py | 33 ------------- .../compressed_tensors_w8a8_statictensor.py | 47 ------------------- 5 files changed, 36 insertions(+), 91 deletions(-) delete mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py delete mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index e88bbc361a5e0..488b4196d0ecb 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -9,8 +9,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensorsScheme, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8DynamicToken, CompressedTensorsW8A8StaticTensor, - CompressedTensorsWNA16) + CompressedTensorsW8A8, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( CompressionFormat, QuantizationArgs, QuantizationStrategy, find_first_name_or_class_match) @@ -150,12 +149,12 @@ def _get_schema(self, weight_quant: BaseModel, if self.quant_format == CompressionFormat.int_quantized.value: if self._is_static_tensor_w8a8(weight_quant, input_quant): - return CompressedTensorsW8A8StaticTensor( - strategy=weight_quant.strategy) + return CompressedTensorsW8A8(strategy=weight_quant.strategy, + input_dynamic=False) if self._is_dynamic_token_w8a8(weight_quant, input_quant): - return CompressedTensorsW8A8DynamicToken( - strategy=weight_quant.strategy) + return CompressedTensorsW8A8(strategy=weight_quant.strategy, + input_dynamic=True) raise NotImplementedError( "No compressed-tensors compatible scheme was found.") diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index f6d20ce2c6f77..c17284247490b 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -3,9 +3,7 @@ CompressedTensorsUnquantized) from .compressed_tensors_w4a16_24 import ( # noqa: F401 W4A16SPARSE24_SUPPORTED_BITS, CompressedTensorsW4A16Sparse24) -from .compressed_tensors_w8a8_dynamictoken import ( # noqa: F401, E501 - CompressedTensorsW8A8DynamicToken) -from .compressed_tensors_w8a8_statictensor import ( # noqa: F401, E501 - CompressedTensorsW8A8StaticTensor) +from .compressed_tensors_w8a8 import ( # noqa: F401 + CompressedTensorsW8A8) from .compressed_tensors_wNa16 import WNA16_SUPPORTED_BITS # noqa: F401 from .compressed_tensors_wNa16 import CompressedTensorsWNA16 # noqa: F401 diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 49779057659f0..cbd9a24dc6fa9 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -3,6 +3,7 @@ import torch from torch.nn import Parameter +from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( @@ -12,8 +13,9 @@ class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, strategy: str): + def __init__(self, strategy: str, input_dynamic: bool): self.strategy = strategy + self.input_dynamic = input_dynamic # Cutlass kernels support only per-tensor and per-channel cases. # So if we have a fused module (QKV, MLP) with per tensor scales (thus N @@ -75,3 +77,29 @@ def create_weights(self, layer: torch.nn.Module, "output_dim": 0, "weight_loader": weight_loader, }) + + # INPUT SCALE + if self.input_dynamic: + # Dynamic quantization: set to None. + layer.input_scale = None + else: + # Static quantization: load from disk. + input_scale = Parameter(torch.empty(1, dtype=torch.float32), + requires_grad=False) + layer.register_parameter("input_scale", input_scale) + set_weight_attrs(input_scale, { + "weight_loader": weight_loader, + "ignore_warning": True, + }) + + def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): + # ops.scaled_fp8_quant supports both dynamic and static quant. + # * dynamic, layer.input_scale is None and x_scale computed from x. + # * static, layer.input_scale is scalar and x_scale is input_scale. + x_q, x_scale = ops.scaled_int8_quant(x, layer.input_scale) + + return ops.cutlass_scaled_mm(x_q, + layer.weight.t(), + scale_a=x_scale, + scale_b=layer.weight_scale, + out_dtype=x.dtype) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py deleted file mode 100644 index 5fc05b8e682d6..0000000000000 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import Callable, List - -import torch - -from vllm import _custom_ops as custom_ops -from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w8a8 import ( # noqa: E501 - CompressedTensorsW8A8) - -__all__ = ["CompressedTensorsW8A8DynamicToken"] - - -class CompressedTensorsW8A8DynamicToken(CompressedTensorsW8A8): - - def create_weights(self, layer: torch.nn.Module, - output_partition_sizes: List[int], - input_size_per_partition: int, - params_dtype: torch.dtype, weight_loader: Callable, - **kwargs): - - super().create_weights( - layer=layer, - output_partition_sizes=output_partition_sizes, - input_size_per_partition=input_size_per_partition, - params_dtype=params_dtype, - weight_loader=weight_loader) - - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - weight = layer.weight - weight_scale = layer.weight_scale - - x_q, input_scales = custom_ops.scaled_int8_quant(x) - return custom_ops.cutlass_scaled_mm(x_q, weight.t(), input_scales, - weight_scale, x.dtype) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py deleted file mode 100644 index 79f5358a365ed..0000000000000 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py +++ /dev/null @@ -1,47 +0,0 @@ -from typing import Callable, List - -import torch -from torch.nn import Parameter - -from vllm import _custom_ops as custom_ops -from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w8a8 import ( # noqa: E501 - CompressedTensorsW8A8) -from vllm.model_executor.utils import set_weight_attrs - -__all__ = ["CompressedTensorsW8A8StaticTensor"] - - -class CompressedTensorsW8A8StaticTensor(CompressedTensorsW8A8): - - def create_weights(self, layer: torch.nn.Module, - output_partition_sizes: List[int], - input_size_per_partition: int, - params_dtype: torch.dtype, weight_loader: Callable, - **kwargs): - - super().create_weights( - layer=layer, - output_partition_sizes=output_partition_sizes, - input_size_per_partition=input_size_per_partition, - params_dtype=params_dtype, - weight_loader=weight_loader) - - input_scale = Parameter(torch.empty(1, dtype=torch.float32), - requires_grad=False) - - layer.register_parameter("input_scale", input_scale) - set_weight_attrs(input_scale, { - "weight_loader": weight_loader, - "ignore_warning": True, - }) - - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - weight = layer.weight - weight_scale = layer.weight_scale - act_scale = layer.input_scale - - # Input quantize - x_q, _ = custom_ops.scaled_int8_quant(x, act_scale) - - return custom_ops.cutlass_scaled_mm(x_q, weight.t(), act_scale, - weight_scale, x.dtype) From aa564759287c1061353e9af8ec0716c3a4032f8e Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 18:56:14 +0000 Subject: [PATCH 027/114] updated to transpose in process_after_loading --- .../compressed_tensors/schemes/compressed_tensors_w8a8.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index cbd9a24dc6fa9..e73fbf0166d45 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -37,6 +37,10 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None: layer.weight_scale = Parameter(weight_scale_channel, requires_grad=False) + + # transpose weights for cutlass. + weight = layer.weight + layer.weight = Parameter(weight.t(), requires_grad=False) def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], @@ -99,7 +103,7 @@ def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): x_q, x_scale = ops.scaled_int8_quant(x, layer.input_scale) return ops.cutlass_scaled_mm(x_q, - layer.weight.t(), + layer.weight, scale_a=x_scale, scale_b=layer.weight_scale, out_dtype=x.dtype) From d94d07e6bfefe984bb639e51f15bb64472c7d8c0 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 19:11:15 +0000 Subject: [PATCH 028/114] updated with varuns suggestion --- .../compressed_tensors/compressed_tensors.py | 4 ++-- .../schemes/compressed_tensors_w8a8.py | 16 ++++++++-------- 2 files changed, 10 insertions(+), 10 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 488b4196d0ecb..e57aedb198cc0 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -150,11 +150,11 @@ def _get_schema(self, weight_quant: BaseModel, if self.quant_format == CompressionFormat.int_quantized.value: if self._is_static_tensor_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8(strategy=weight_quant.strategy, - input_dynamic=False) + is_static_activations=False) if self._is_dynamic_token_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8(strategy=weight_quant.strategy, - input_dynamic=True) + is_static_activations=False) raise NotImplementedError( "No compressed-tensors compatible scheme was found.") diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index e73fbf0166d45..2dbecd1d000c7 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -13,9 +13,9 @@ class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, strategy: str, input_dynamic: bool): + def __init__(self, strategy: str, is_static_activations: bool): self.strategy = strategy - self.input_dynamic = input_dynamic + self.is_static_activations = is_static_activations # Cutlass kernels support only per-tensor and per-channel cases. # So if we have a fused module (QKV, MLP) with per tensor scales (thus N @@ -83,18 +83,18 @@ def create_weights(self, layer: torch.nn.Module, }) # INPUT SCALE - if self.input_dynamic: - # Dynamic quantization: set to None. - layer.input_scale = None - else: - # Static quantization: load from disk. + # Static quantization: load from disk. + if self.is_static_activations: input_scale = Parameter(torch.empty(1, dtype=torch.float32), - requires_grad=False) + requires_grad=False) layer.register_parameter("input_scale", input_scale) set_weight_attrs(input_scale, { "weight_loader": weight_loader, "ignore_warning": True, }) + # Dynamic quantization: set to None. + else: + layer.input_scale = None def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): # ops.scaled_fp8_quant supports both dynamic and static quant. From 54308d7ef75037cd0d17979b6c4d5827c8acd2bb Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 19:12:11 +0000 Subject: [PATCH 029/114] fixed nit --- .../compressed_tensors/schemes/compressed_tensors_w8a8.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 2dbecd1d000c7..6fdba6b8cfd78 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -97,7 +97,7 @@ def create_weights(self, layer: torch.nn.Module, layer.input_scale = None def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - # ops.scaled_fp8_quant supports both dynamic and static quant. + # ops.scaled_int8_quant supports both dynamic and static quant. # * dynamic, layer.input_scale is None and x_scale computed from x. # * static, layer.input_scale is scalar and x_scale is input_scale. x_q, x_scale = ops.scaled_int8_quant(x, layer.input_scale) From 173b93ba9aea870e73a6247425df27086930ae10 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 19:13:23 +0000 Subject: [PATCH 030/114] name change --- .../quantization/compressed_tensors/compressed_tensors.py | 4 ++-- .../compressed_tensors/schemes/compressed_tensors_w8a8.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index e57aedb198cc0..55d4f62c4b6de 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -150,11 +150,11 @@ def _get_schema(self, weight_quant: BaseModel, if self.quant_format == CompressionFormat.int_quantized.value: if self._is_static_tensor_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8(strategy=weight_quant.strategy, - is_static_activations=False) + is_static_input_scheme=False) if self._is_dynamic_token_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8(strategy=weight_quant.strategy, - is_static_activations=False) + is_static_input_scheme=False) raise NotImplementedError( "No compressed-tensors compatible scheme was found.") diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 6fdba6b8cfd78..973e186597e37 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -13,9 +13,9 @@ class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, strategy: str, is_static_activations: bool): + def __init__(self, strategy: str, is_static_input_scheme: bool): self.strategy = strategy - self.is_static_activations = is_static_activations + self.is_static_input_scheme = is_static_input_scheme # Cutlass kernels support only per-tensor and per-channel cases. # So if we have a fused module (QKV, MLP) with per tensor scales (thus N @@ -84,7 +84,7 @@ def create_weights(self, layer: torch.nn.Module, # INPUT SCALE # Static quantization: load from disk. - if self.is_static_activations: + if self.is_static_input_scheme: input_scale = Parameter(torch.empty(1, dtype=torch.float32), requires_grad=False) layer.register_parameter("input_scale", input_scale) From afa1ee11c59ff88138faf099b4b9a2a829831ae6 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 19:15:05 +0000 Subject: [PATCH 031/114] format --- .../quantization/compressed_tensors/schemes/__init__.py | 3 +-- .../compressed_tensors/schemes/compressed_tensors_w8a8.py | 6 +++--- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index c17284247490b..720b8c263298c 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -3,7 +3,6 @@ CompressedTensorsUnquantized) from .compressed_tensors_w4a16_24 import ( # noqa: F401 W4A16SPARSE24_SUPPORTED_BITS, CompressedTensorsW4A16Sparse24) -from .compressed_tensors_w8a8 import ( # noqa: F401 - CompressedTensorsW8A8) +from .compressed_tensors_w8a8 import CompressedTensorsW8A8 # noqa: F401 from .compressed_tensors_wNa16 import WNA16_SUPPORTED_BITS # noqa: F401 from .compressed_tensors_wNa16 import CompressedTensorsWNA16 # noqa: F401 diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 973e186597e37..dffe2a284458f 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -37,7 +37,7 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None: layer.weight_scale = Parameter(weight_scale_channel, requires_grad=False) - + # transpose weights for cutlass. weight = layer.weight layer.weight = Parameter(weight.t(), requires_grad=False) @@ -81,7 +81,7 @@ def create_weights(self, layer: torch.nn.Module, "output_dim": 0, "weight_loader": weight_loader, }) - + # INPUT SCALE # Static quantization: load from disk. if self.is_static_input_scheme: @@ -95,7 +95,7 @@ def create_weights(self, layer: torch.nn.Module, # Dynamic quantization: set to None. else: layer.input_scale = None - + def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): # ops.scaled_int8_quant supports both dynamic and static quant. # * dynamic, layer.input_scale is None and x_scale computed from x. From 5ffe0e45fbffc5002027e59916f786ef0fac7f2c Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 19:15:58 +0000 Subject: [PATCH 032/114] fixed --- .../quantization/compressed_tensors/compressed_tensors.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 55d4f62c4b6de..8ca486d95941d 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -150,7 +150,7 @@ def _get_schema(self, weight_quant: BaseModel, if self.quant_format == CompressionFormat.int_quantized.value: if self._is_static_tensor_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8(strategy=weight_quant.strategy, - is_static_input_scheme=False) + is_static_input_scheme=True) if self._is_dynamic_token_w8a8(weight_quant, input_quant): return CompressedTensorsW8A8(strategy=weight_quant.strategy, From 4c0e56586ef19476a0a8fa4beec9931e0e4fa277 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 19:52:34 +0000 Subject: [PATCH 033/114] fixed tests --- tests/quantization/test_compressed_tensors.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index d5472f97a1c50..4cdda97dc728d 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -9,8 +9,7 @@ from vllm import SamplingParams from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import ( # noqa: E501 CompressedTensorsLinearMethod, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8DynamicToken, CompressedTensorsW8A8StaticTensor, - CompressedTensorsWNA16) + CompressedTensorsW8A8, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( QuantizationType) @@ -38,9 +37,10 @@ def test_compressed_tensors_w8a8_static_setup(vllm_runner, model_args): CompressedTensorsLinearMethod) assert isinstance(down_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8StaticTensor) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8) assert qkv_proj.scheme.strategy == strategy + assert qkv_proj.scheme.is_static_input_scheme expected_type = (torch.int8 if quant_type == QuantizationType.INT else torch.float8_e4m3fn) @@ -79,7 +79,8 @@ def test_compressed_tensors_w8a8_dynanmic_per_token(vllm_runner, model_args): qkv_proj = layer.self_attn.qkv_proj assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8DynamicToken) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8) + assert not qkv_proj.scheme.is_static_input_scheme assert qkv_proj.scheme.strategy == strategy assert qkv_proj.weight.dtype is torch.int8 From 282a038d88c408ea031d3abe667f5fc374e43292 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 22:36:28 +0000 Subject: [PATCH 034/114] merge w8a8 unify --- .../schemes/compressed_tensors_w8a8.py | 34 +++++++++++++- .../compressed_tensors_w8a8_dynamictoken.py | 33 ------------- .../compressed_tensors_w8a8_statictensor.py | 47 ------------------- 3 files changed, 33 insertions(+), 81 deletions(-) delete mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py delete mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py index 49779057659f0..dffe2a284458f 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py @@ -3,6 +3,7 @@ import torch from torch.nn import Parameter +from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( @@ -12,8 +13,9 @@ class CompressedTensorsW8A8(CompressedTensorsScheme): - def __init__(self, strategy: str): + def __init__(self, strategy: str, is_static_input_scheme: bool): self.strategy = strategy + self.is_static_input_scheme = is_static_input_scheme # Cutlass kernels support only per-tensor and per-channel cases. # So if we have a fused module (QKV, MLP) with per tensor scales (thus N @@ -36,6 +38,10 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None: layer.weight_scale = Parameter(weight_scale_channel, requires_grad=False) + # transpose weights for cutlass. + weight = layer.weight + layer.weight = Parameter(weight.t(), requires_grad=False) + def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], input_size_per_partition: int, @@ -75,3 +81,29 @@ def create_weights(self, layer: torch.nn.Module, "output_dim": 0, "weight_loader": weight_loader, }) + + # INPUT SCALE + # Static quantization: load from disk. + if self.is_static_input_scheme: + input_scale = Parameter(torch.empty(1, dtype=torch.float32), + requires_grad=False) + layer.register_parameter("input_scale", input_scale) + set_weight_attrs(input_scale, { + "weight_loader": weight_loader, + "ignore_warning": True, + }) + # Dynamic quantization: set to None. + else: + layer.input_scale = None + + def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): + # ops.scaled_int8_quant supports both dynamic and static quant. + # * dynamic, layer.input_scale is None and x_scale computed from x. + # * static, layer.input_scale is scalar and x_scale is input_scale. + x_q, x_scale = ops.scaled_int8_quant(x, layer.input_scale) + + return ops.cutlass_scaled_mm(x_q, + layer.weight, + scale_a=x_scale, + scale_b=layer.weight_scale, + out_dtype=x.dtype) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py deleted file mode 100644 index 5fc05b8e682d6..0000000000000 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_dynamictoken.py +++ /dev/null @@ -1,33 +0,0 @@ -from typing import Callable, List - -import torch - -from vllm import _custom_ops as custom_ops -from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w8a8 import ( # noqa: E501 - CompressedTensorsW8A8) - -__all__ = ["CompressedTensorsW8A8DynamicToken"] - - -class CompressedTensorsW8A8DynamicToken(CompressedTensorsW8A8): - - def create_weights(self, layer: torch.nn.Module, - output_partition_sizes: List[int], - input_size_per_partition: int, - params_dtype: torch.dtype, weight_loader: Callable, - **kwargs): - - super().create_weights( - layer=layer, - output_partition_sizes=output_partition_sizes, - input_size_per_partition=input_size_per_partition, - params_dtype=params_dtype, - weight_loader=weight_loader) - - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - weight = layer.weight - weight_scale = layer.weight_scale - - x_q, input_scales = custom_ops.scaled_int8_quant(x) - return custom_ops.cutlass_scaled_mm(x_q, weight.t(), input_scales, - weight_scale, x.dtype) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py deleted file mode 100644 index 79f5358a365ed..0000000000000 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_statictensor.py +++ /dev/null @@ -1,47 +0,0 @@ -from typing import Callable, List - -import torch -from torch.nn import Parameter - -from vllm import _custom_ops as custom_ops -from vllm.model_executor.layers.quantization.compressed_tensors.schemes.compressed_tensors_w8a8 import ( # noqa: E501 - CompressedTensorsW8A8) -from vllm.model_executor.utils import set_weight_attrs - -__all__ = ["CompressedTensorsW8A8StaticTensor"] - - -class CompressedTensorsW8A8StaticTensor(CompressedTensorsW8A8): - - def create_weights(self, layer: torch.nn.Module, - output_partition_sizes: List[int], - input_size_per_partition: int, - params_dtype: torch.dtype, weight_loader: Callable, - **kwargs): - - super().create_weights( - layer=layer, - output_partition_sizes=output_partition_sizes, - input_size_per_partition=input_size_per_partition, - params_dtype=params_dtype, - weight_loader=weight_loader) - - input_scale = Parameter(torch.empty(1, dtype=torch.float32), - requires_grad=False) - - layer.register_parameter("input_scale", input_scale) - set_weight_attrs(input_scale, { - "weight_loader": weight_loader, - "ignore_warning": True, - }) - - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - weight = layer.weight - weight_scale = layer.weight_scale - act_scale = layer.input_scale - - # Input quantize - x_q, _ = custom_ops.scaled_int8_quant(x, act_scale) - - return custom_ops.cutlass_scaled_mm(x_q, weight.t(), act_scale, - weight_scale, x.dtype) From a0fd035a8f189a30536f7b90bf763d56679389bb Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 22:42:20 +0000 Subject: [PATCH 035/114] fix nit --- .../quantization/compressed_tensors/schemes/__init__.py | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index 95ad3cc65809e..6f5547f79aa31 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -1,17 +1,18 @@ from .compressed_tensors_scheme import CompressedTensorsScheme from .compressed_tensors_unquantized import CompressedTensorsUnquantized -from .compressed_tensors_w4a16_24 import CompressedTensorsW4A16Sparse24, W4A16SPARSE24_SUPPORTED_BITS, +from .compressed_tensors_w4a16_24 import (CompressedTensorsW4A16Sparse24, + W4A16SPARSE24_SUPPORTED_BITS) from .compressed_tensors_w8a8 import CompressedTensorsW8A8 from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8 -from .compressed_tensors_wNa16 import CompressedTensorsWNA16 +from .compressed_tensors_wNa16 import (CompressedTensorsWNA16, + WNA16_SUPPORTED_BITS) __all__ = [ "CompressedTensorsScheme", "CompressedTensorsUnquantized", "CompressedTensorsWNA16", "CompressedTensorsW4A16Sparse24", - "CompressedTensorsW8A8DynamicToken", - "CompressedTensorsW8A8StaticTensor", + "CompressedTensorsW8A8", "CompressedTensorsW8A8Fp8", "WNA16_SUPPORTED_BITS", "W4A16SPARSE24_SUPPORTED_BITS", From ba1116b6daca40c7c638717996d975939c727fcc Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 22:43:04 +0000 Subject: [PATCH 036/114] nits --- .../layers/quantization/compressed_tensors/schemes/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index 6f5547f79aa31..b0076b0167616 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -16,4 +16,4 @@ "CompressedTensorsW8A8Fp8", "WNA16_SUPPORTED_BITS", "W4A16SPARSE24_SUPPORTED_BITS", -] \ No newline at end of file +] From c1d4375cda5117324573d4b890669d72f980cffa Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Wed, 3 Jul 2024 22:45:35 +0000 Subject: [PATCH 037/114] cleanup --- .../compressed_tensors/schemes/compressed_tensors_scheme.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py index a480f7ca8386c..119f6cd91bb0c 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_scheme.py @@ -38,4 +38,4 @@ def process_weights_after_loading(self, layer: torch.nn.Module): Called after weight loading is complete for any cleanup that needs to occur. """ - pass + raise NotImplementedError From a12bfd56543b0911a9dd57bb324b71846cba2802 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sat, 6 Jul 2024 12:43:43 +0000 Subject: [PATCH 038/114] stash --- .../schemes/compressed_tensors_w8a8_fp8.py | 39 +++--- .../layers/quantization/utils/fp8_utils.py | 116 ------------------ 2 files changed, 21 insertions(+), 134 deletions(-) delete mode 100644 vllm/model_executor/layers/quantization/utils/fp8_utils.py diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index bcd3d34a90314..4ad17e60f0a1c 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -18,6 +18,27 @@ def __init__(self, input_dynamic: bool): self.input_dynamic = input_dynamic self.cutlass_fp8_supported = cutlass_fp8_supported() + # W8A8-Fp8 kernels support only per-tensor and per-channel cases. + # So if we have a fused module (QKV, MLP) with per tensor scales (thus N + # scales being passed to the kernel), we requantize with a single scale. + def process_weights_after_loading(self, layer) -> None: + # Dequant -> Quant with max scale. + max_w_scale, weight = requantize_with_max_scale( + weight=layer.weight, + weight_scale=layer.weight_scale, + logical_widths=layer.logical_widths, + ) + + # Update layer with new values. + layer.weight = torch.nn.Parameter(weight.t(), requires_grad=False) + layer.weight_scale = torch.nn.Parameter(max_w_scale, + requires_grad=False) + if self.input_dynamic: + layer.input_scale = None + else: + layer.input_scale = torch.nn.Parameter(layer.input_scale.max(), + requires_grad=False) + def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], input_size_per_partition: int, @@ -52,24 +73,6 @@ def create_weights(self, layer: torch.nn.Module, weight_loader=weight_loader) layer.register_parameter("input_scale", input_scale) - def process_weights_after_loading(self, layer) -> None: - # Dequant -> Quant with max scale. - max_w_scale, weight = requantize_with_max_scale( - weight=layer.weight, - weight_scale=layer.weight_scale, - logical_widths=layer.logical_widths, - ) - - # Update layer with new values. - layer.weight = torch.nn.Parameter(weight.t(), requires_grad=False) - layer.weight_scale = torch.nn.Parameter(max_w_scale, - requires_grad=False) - if self.input_dynamic: - layer.input_scale = None - else: - layer.input_scale = torch.nn.Parameter(layer.input_scale.max(), - requires_grad=False) - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor, diff --git a/vllm/model_executor/layers/quantization/utils/fp8_utils.py b/vllm/model_executor/layers/quantization/utils/fp8_utils.py deleted file mode 100644 index 81dec7ffab5bb..0000000000000 --- a/vllm/model_executor/layers/quantization/utils/fp8_utils.py +++ /dev/null @@ -1,116 +0,0 @@ -from typing import List, Optional, Tuple, Union - -import torch - -from vllm import _custom_ops as ops -from vllm.model_executor.utils import set_weight_attrs -from vllm.platforms import current_platform - - -def cutlass_fp8_supported() -> bool: - capability = current_platform.get_device_capability() - capability = capability[0] * 10 + capability[1] - - return ops.cutlass_scaled_mm_supports_fp8(capability) - - -def per_tensor_quantize(tensor: torch.Tensor, - inv_scale: Union[float, torch.Tensor]) -> torch.Tensor: - finfo = torch.finfo(torch.float8_e4m3fn) - qweight = (tensor / inv_scale).clamp(min=finfo.min, max=finfo.max) - return qweight.to(torch.float8_e4m3fn) - - -def per_tensor_dequantize( - tensor: torch.Tensor, inv_scale: Union[float, - torch.Tensor]) -> torch.Tensor: - fake_qweight = tensor.to(torch.float16) - dq_weight = fake_qweight * inv_scale - return dq_weight - - -def all_close_1d(x: torch.Tensor) -> bool: - assert len(x.shape) == 1 - return all(torch.allclose(x[0], x[i]) for i in range(x.shape[0])) - - -def create_scale_param(output_partition_sizes: List[int], - **extra_weight_attrs) -> torch.nn.Parameter: - scale = torch.nn.Parameter(torch.empty(len(output_partition_sizes), - dtype=torch.float32), - requires_grad=False) - scale[:] = torch.finfo(torch.float8_e4m3fn).min - set_weight_attrs(scale, { - **extra_weight_attrs, - "needs_scalar_to_array": True, - }) - return scale - - -def requantize_with_max_scale( - weight: torch.Tensor, weight_scale: torch.Tensor, - logical_widths: List[int]) -> Tuple[torch.Tensor, torch.Tensor]: - # Max scale to be used for requanitzation. - max_w_scale = weight_scale.max() - - # QKV / MLP is fused in the on disk checkpoint if any of the - # weight scales are still set to the default since we initialize - # N weight scales for N shards but we only load 1 weight scale - # from disk in this case. Skip requantization in this case (since) - # we already are quantized with the single scale. - # * Sample Model: nm-testing/Phi-3-mini-128k-instruct-FP8 - unfused_module_in_checkpoint = (weight_scale[-1] > torch.finfo( - torch.float8_e4m3fn).min) - - # If unfused checkpoint, need requanize with the single scale. - if unfused_module_in_checkpoint: - start = 0 - for idx, logical_width in enumerate(logical_widths): - end = start + logical_width - weight_dq = per_tensor_dequantize(weight[start:end, :], - weight_scale[idx]) - weight[start:end, :] = per_tensor_quantize(weight_dq, max_w_scale) - start = end - - return max_w_scale, weight - - -def fp8_apply( - input: torch.Tensor, - weight: torch.Tensor, - weight_scale: torch.Tensor, - input_scale: torch.Tensor, - bias: Optional[torch.Tensor] = None, - cutlass_fp8_supported: bool = True, -): - # ops.scaled_fp8_quant supports both dynamic and static quant. - # If dynamic, layer.input_scale is None and x_scale computed from x. - # If static, layer.input_scale is scalar and x_scale is input_scale. - - if bias is None and cutlass_fp8_supported: - qinput, x_scale = ops.scaled_fp8_quant(input, input_scale) - - # Fused GEMM_DQ - output = ops.cutlass_scaled_mm(qinput, - weight, - out_dtype=input.dtype, - scale_a=x_scale, - scale_b=weight_scale) - - else: - qinput, x_scale = ops.scaled_fp8_quant(input, - input_scale, - batch_dim_padding=17) - - # Fused GEMM_DQ -- note we padded the input above because - # torch._scaled_mm is more performant for matrices with - # batch dimension > 16. Note that this could change - # in the future. - output, _ = torch._scaled_mm(qinput, - weight, - out_dtype=input.dtype, - scale_a=x_scale, - scale_b=weight_scale, - bias=bias) - - return torch.narrow(output, 0, 0, input.shape[0]) From 4fc017700101a4f8290fd746363d62a4bf713c9f Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sat, 6 Jul 2024 13:31:46 +0000 Subject: [PATCH 039/114] autofp8 working --- .../schemes/compressed_tensors_w8a8_fp8.py | 26 ++-- .../model_executor/layers/quantization/fp8.py | 135 ++++-------------- .../layers/quantization/gptq_marlin.py | 13 +- .../layers/quantization/utils/marlin_utils.py | 97 ++++++++++++- 4 files changed, 141 insertions(+), 130 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index 4ad17e60f0a1c..bcd05a9587d94 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -4,9 +4,9 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) -from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - create_scale_param, cutlass_fp8_supported, fp8_apply, - requantize_with_max_scale) +from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( + create_per_tensor_scale_param, cutlass_fp8_supported, + apply_fp8_linear, requantize_with_max_scale) from vllm.model_executor.utils import set_weight_attrs __all__ = ["CompressedTensorsW8A8Fp8"] @@ -63,14 +63,14 @@ def create_weights(self, layer: torch.nn.Module, }) # WEIGHT SCALE - weight_scale = create_scale_param(output_partition_sizes, - weight_loader=weight_loader) + weight_scale = create_per_tensor_scale_param( + output_partition_sizes, weight_loader=weight_loader) layer.register_parameter("weight_scale", weight_scale) # INPUT SCALE if not self.input_dynamic: - input_scale = create_scale_param(output_partition_sizes, - weight_loader=weight_loader) + input_scale = create_per_tensor_scale_param( + output_partition_sizes, weight_loader=weight_loader) layer.register_parameter("input_scale", input_scale) def apply_weights(self, @@ -78,9 +78,9 @@ def apply_weights(self, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - return fp8_apply(input=x, - weight=layer.weight, - weight_scale=layer.weight_scale, - input_scale=layer.input_scale, - bias=bias, - cutlass_fp8_supported=self.cutlass_fp8_supported) + return apply_fp8_linear(input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, + bias=bias, + cutlass_fp8_supported=self.cutlass_fp8_supported) diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index b1de91465e153..61cfda1e6f94a 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -11,15 +11,15 @@ from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) -from vllm.model_executor.layers.quantization.utils.fp8_utils import ( - all_close_1d, create_scale_param, cutlass_fp8_supported, fp8_apply, - per_tensor_dequantize, per_tensor_quantize, requantize_with_max_scale) -from vllm.model_executor.layers.quantization.gptq_marlin import ( - GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, GPTQMarlinState, - marlin_permute_scales) +from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( + all_close_1d, create_per_tensor_scale_param, cutlass_fp8_supported, + apply_fp8_linear, per_tensor_dequantize, per_tensor_quantize, + requantize_with_max_scale) from vllm.model_executor.layers.quantization.utils.marlin_utils import ( - pack_fp8_to_int32) + prepare_fp8_layer_for_marlin, apply_fp8_marlin_linear, +) from vllm.model_executor.utils import set_weight_attrs +from vllm.platforms import current_platform from vllm.utils import print_warning_once ACTIVATION_SCHEMES = ["static", "dynamic"] @@ -150,77 +150,17 @@ def create_weights( # Otherwise, wait until process_weights_after_loading. if self.quant_config.is_checkpoint_fp8_serialized: # WEIGHT SCALE - scale = create_scale_param(output_partition_sizes, - **extra_weight_attrs) + scale = create_per_tensor_scale_param(output_partition_sizes, + **extra_weight_attrs) layer.register_parameter("weight_scale", scale) # INPUT ACTIVATION SCALE if self.quant_config.activation_scheme == "static": - scale = create_scale_param(output_partition_sizes, - **extra_weight_attrs) + scale = create_per_tensor_scale_param(output_partition_sizes, + **extra_weight_attrs) layer.register_parameter("input_scale", scale) - # For GPUs without FP8 hardware support, we use Marlin for fast - # fused dequantization - if self.use_marlin: - layer.marlin_state = GPTQMarlinState.REPACK - - def prepare_layer_for_marlin(self, layer: Module) -> None: - print_warning_once( - "Your GPU does not have native support for FP8 computation but " - "FP8 quantization is being used. Weight-only FP8 compression will " - "be used leveraging the Marlin kernel. This may degrade " - "performance for compute-heavy workloads.") - - part_size_n = layer.output_size_per_partition - part_size_k = layer.input_size_per_partition - - assert layer.marlin_state == GPTQMarlinState.REPACK - layer.marlin_state = GPTQMarlinState.READY - - device = layer.weight.device - - # WEIGHTS - # Repack weights to gptq format (packed int32 elements) - packed_gptq_qweight = pack_fp8_to_int32(layer.weight) - - # Repack weights to marlin format - marlin_qweight = ops.gptq_marlin_repack( - b_q_weight=packed_gptq_qweight, - perm=torch.empty(0, dtype=torch.int, device=device), - size_k=part_size_k, - size_n=part_size_n, - num_bits=8, - ) - layer.weight = Parameter(marlin_qweight, requires_grad=False) - - # WEIGHT SCALES - # Currently Marlin doesn't support per-tensor scales, so we - # expand it to channelwise - scales = layer.weight_scale.repeat(1, part_size_n).to( - layer.orig_dtype).to(device) - # Permute scales - marlin_scales = marlin_permute_scales( - s=scales, - size_k=part_size_k, - size_n=part_size_n, - group_size=-1, - num_bits=8, - ) - layer.weight_scale = Parameter(marlin_scales, requires_grad=False) - - # Allocate marlin workspace - max_workspace_size = ( - part_size_n // GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL - workspace = torch.zeros(max_workspace_size, - dtype=torch.int, - device=device, - requires_grad=False) - - layer.workspace = workspace - def process_weights_after_loading(self, layer: Module) -> None: - # If checkpoint not serialized fp8, quantize the weights. if not self.quant_config.is_checkpoint_fp8_serialized: qweight, weight_scale = ops.scaled_fp8_quant(layer.weight, @@ -230,9 +170,6 @@ def process_weights_after_loading(self, layer: Module) -> None: layer.weight = Parameter(qweight.t(), requires_grad=False) layer.weight_scale = Parameter(weight_scale, requires_grad=False) layer.input_scale = None - if self.use_marlin: - self.prepare_layer_for_marlin(layer) - return # If checkpoint is fp8, requantize the separately quantized logical # weights into a single fp8 weight with a single weight scale. @@ -252,46 +189,34 @@ def process_weights_after_loading(self, layer: Module) -> None: requires_grad=False) else: layer.input_scale = None - - if self.use_marlin: - self.prepare_layer_for_marlin(layer) + + if self.use_marlin: + prepare_fp8_layer_for_marlin(layer) + # Activations not quantized for marlin. + del layer.input_scale def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - + if self.use_marlin: - # For GPUs that lack FP8 hardware support, we can leverage the - # Marlin kernel for fast weight-only FP8 quantization - - reshaped_x = x.reshape(-1, x.shape[-1]) - out_shape = x.shape[:-1] + (layer.output_size_per_partition, ) - - output = ops.fp8_marlin_gemm( - a=reshaped_x, - b_q_weight=layer.weight, - b_scales=layer.weight_scale, + return apply_fp8_marlin_linear( + input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, workspace=layer.workspace, - num_bits=8, - size_m=reshaped_x.shape[0], size_n=layer.output_size_per_partition, size_k=layer.input_size_per_partition, - ) - - if bias is not None: - output.add_(bias) # In-place add - - return output.reshape(out_shape) - - - - return fp8_apply(input=x, - weight=layer.weight, - weight_scale=layer.weight_scale, - input_scale=layer.input_scale, - bias=bias, - cutlass_fp8_supported=self.cutlass_fp8_supported) + bias=bias) + + return apply_fp8_linear( + input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, + bias=bias, + cutlass_fp8_supported=self.cutlass_fp8_supported) class Fp8MoEMethod(FusedMoEMethodBase): diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index a6284d0ed7b1b..075a87bc5b294 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -13,18 +13,13 @@ QuantizationConfig) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.platforms import current_platform +from vllm.model_executor.layers.quantization.utils.marlin_utils import ( + GPTQ_MARLIN_TILE, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MIN_THREAD_K, + GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_SUPPORTED_NUM_BITS, + GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_SYM) logger = init_logger(__name__) -GPTQ_MARLIN_TILE = 16 -GPTQ_MARLIN_MIN_THREAD_N = 64 -GPTQ_MARLIN_MIN_THREAD_K = 128 -GPTQ_MARLIN_MAX_PARALLEL = 16 - -GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8] -GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128] -GPTQ_MARLIN_SUPPORTED_SYM = [True] - # Permutations for Marlin scale shuffling def get_scale_perms(num_bits: int): diff --git a/vllm/model_executor/layers/quantization/utils/marlin_utils.py b/vllm/model_executor/layers/quantization/utils/marlin_utils.py index 66ce1959207cc..d2b8038602cc4 100644 --- a/vllm/model_executor/layers/quantization/utils/marlin_utils.py +++ b/vllm/model_executor/layers/quantization/utils/marlin_utils.py @@ -1,8 +1,8 @@ """This file is used for /tests and /benchmarks""" import random - import numpy import torch +from typing import Optional from vllm.model_executor.layers.quantization.utils.format_24 import ( mask_creator, sparse_semi_structured_from_dense_cutlass) @@ -13,8 +13,17 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import ( get_pack_factor, quantize_weights, sort_weights) from vllm.platforms import current_platform +from vllm import _custom_ops as ops +from vllm.utils import print_warning_once + +GPTQ_MARLIN_TILE = 16 +GPTQ_MARLIN_MIN_THREAD_N = 64 +GPTQ_MARLIN_MIN_THREAD_K = 128 +GPTQ_MARLIN_MAX_PARALLEL = 16 -MARLIN_TILE = 16 +GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8] +GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128] +GPTQ_MARLIN_SUPPORTED_SYM = [True] def is_marlin_supported(): @@ -22,7 +31,89 @@ def is_marlin_supported(): return capability[0] >= 8 -def marlin_permute_weights(q_w, size_k, size_n, perm, tile=MARLIN_TILE): +def apply_fp8_marlin_linear( + input: torch.Tensor, + weight: torch.Tensor, + weight_scale: torch.Tensor, + workspace: torch.Tensor, + size_n: int, + size_k: int, + bias: Optional[torch.Tensor], +) -> torch.Tensor: + # For GPUs that lack FP8 hardware support, we can leverage the + # Marlin kernel for fast weight-only FP8 quantization + + reshaped_x = input.reshape(-1, input.shape[-1]) + out_shape = input.shape[:-1] + (size_n, ) + + output = ops.fp8_marlin_gemm(a=reshaped_x, + b_q_weight=weight, + b_scales=weight_scale, + workspace=workspace, + num_bits=8, + size_m=reshaped_x.shape[0], + size_n=size_n, + size_k=size_k,) + + if bias is not None: + output.add_(bias) # In-place add + + return output.reshape(out_shape) + + +def prepare_fp8_layer_for_marlin(layer: torch.nn.Module) -> None: + print_warning_once( + "Your GPU does not have native support for FP8 computation but " + "FP8 quantization is being used. Weight-only FP8 compression will " + "be used leveraging the Marlin kernel. This may degrade " + "performance for compute-heavy workloads.") + + part_size_n = layer.output_size_per_partition + part_size_k = layer.input_size_per_partition + + device = layer.weight.device + + # WEIGHTS + # Repack weights to gptq format (packed int32 elements) + packed_gptq_qweight = pack_fp8_to_int32(layer.weight) + + # Repack weights to marlin format + marlin_qweight = ops.gptq_marlin_repack( + b_q_weight=packed_gptq_qweight, + perm=torch.empty(0, dtype=torch.int, device=device), + size_k=part_size_k, + size_n=part_size_n, + num_bits=8, + ) + layer.weight = torch.nn.Parameter(marlin_qweight, requires_grad=False) + + # WEIGHT SCALES + # Currently Marlin doesn't support per-tensor scales, so we + # expand it to channelwise + scales = layer.weight_scale.repeat(1, part_size_n).to( + layer.orig_dtype).to(device) + # Permute scales + marlin_scales = marlin_permute_scales( + s=scales, + size_k=part_size_k, + size_n=part_size_n, + group_size=-1, + num_bits=8, + ) + layer.weight_scale = torch.nn.Parameter(marlin_scales, requires_grad=False) + + # Allocate marlin workspace + max_workspace_size = (part_size_n // + GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL + workspace = torch.zeros(max_workspace_size, + dtype=torch.int, + device=device, + requires_grad=False) + + layer.workspace = workspace + + +def marlin_permute_weights(q_w, size_k, size_n, perm, tile=GPTQ_MARLIN_TILE): assert q_w.shape == (size_k, size_n) assert size_k % tile == 0, f"size_k = {size_k}, tile = {tile}" assert size_n % tile == 0, f"size_k = {size_n}, tile = {tile}" From 1d99867d872b49b215b69e22a9a31aa425488495 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sat, 6 Jul 2024 14:54:21 +0000 Subject: [PATCH 040/114] stash --- tests/quantization/test_compressed_tensors.py | 6 +- .../compressed_tensors/compressed_tensors.py | 13 ++- .../compressed_tensors/schemes/__init__.py | 4 +- .../schemes/compressed_tensors_w8a8.py | 109 ------------------ .../schemes/compressed_tensors_w8a8_fp8.py | 17 +-- .../model_executor/layers/quantization/fp8.py | 12 +- .../layers/quantization/gptq_marlin.py | 24 ---- .../layers/quantization/utils/marlin_utils.py | 37 ++++-- 8 files changed, 54 insertions(+), 168 deletions(-) delete mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index 4cdda97dc728d..426d4b51e5759 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -9,7 +9,7 @@ from vllm import SamplingParams from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import ( # noqa: E501 CompressedTensorsLinearMethod, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8, CompressedTensorsWNA16) + CompressedTensorsW8A8Int8, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( QuantizationType) @@ -37,7 +37,7 @@ def test_compressed_tensors_w8a8_static_setup(vllm_runner, model_args): CompressedTensorsLinearMethod) assert isinstance(down_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8) assert qkv_proj.scheme.strategy == strategy assert qkv_proj.scheme.is_static_input_scheme @@ -79,7 +79,7 @@ def test_compressed_tensors_w8a8_dynanmic_per_token(vllm_runner, model_args): qkv_proj = layer.self_attn.qkv_proj assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) - assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Int8) assert not qkv_proj.scheme.is_static_input_scheme assert qkv_proj.scheme.strategy == strategy assert qkv_proj.weight.dtype is torch.int8 diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 413e9762754f8..632006af27baf 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -9,7 +9,8 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensorsScheme, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8, CompressedTensorsW8A8Fp8, CompressedTensorsWNA16) + CompressedTensorsW8A8Int8, CompressedTensorsW8A8Fp8, + CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( CompressionFormat, QuantizationArgs, QuantizationStrategy, QuantizationType, find_first_name_or_class_match) @@ -188,12 +189,14 @@ def _get_schema(self, weight_quant: BaseModel, input_dynamic=input_quant.dynamic) if self._is_static_tensor_w8a8(weight_quant, input_quant): - return CompressedTensorsW8A8(strategy=weight_quant.strategy, - is_static_input_scheme=True) + return CompressedTensorsW8A8Int8( + strategy=weight_quant.strategy, + is_static_input_scheme=True) if self._is_dynamic_token_w8a8(weight_quant, input_quant): - return CompressedTensorsW8A8(strategy=weight_quant.strategy, - is_static_input_scheme=False) + return CompressedTensorsW8A8Int8( + strategy=weight_quant.strategy, + is_static_input_scheme=False) raise NotImplementedError( "No compressed-tensors compatible scheme was found.") diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index b0076b0167616..9d47b80017a59 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -2,7 +2,7 @@ from .compressed_tensors_unquantized import CompressedTensorsUnquantized from .compressed_tensors_w4a16_24 import (CompressedTensorsW4A16Sparse24, W4A16SPARSE24_SUPPORTED_BITS) -from .compressed_tensors_w8a8 import CompressedTensorsW8A8 +from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8 from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8 from .compressed_tensors_wNa16 import (CompressedTensorsWNA16, WNA16_SUPPORTED_BITS) @@ -12,7 +12,7 @@ "CompressedTensorsUnquantized", "CompressedTensorsWNA16", "CompressedTensorsW4A16Sparse24", - "CompressedTensorsW8A8", + "CompressedTensorsW8A8Int8", "CompressedTensorsW8A8Fp8", "WNA16_SUPPORTED_BITS", "W4A16SPARSE24_SUPPORTED_BITS", diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py deleted file mode 100644 index dffe2a284458f..0000000000000 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8.py +++ /dev/null @@ -1,109 +0,0 @@ -from typing import Callable, List, Tuple, Union - -import torch -from torch.nn import Parameter - -from vllm import _custom_ops as ops -from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( - CompressedTensorsScheme) -from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( - QuantizationStrategy) -from vllm.model_executor.utils import set_weight_attrs - - -class CompressedTensorsW8A8(CompressedTensorsScheme): - - def __init__(self, strategy: str, is_static_input_scheme: bool): - self.strategy = strategy - self.is_static_input_scheme = is_static_input_scheme - - # Cutlass kernels support only per-tensor and per-channel cases. - # So if we have a fused module (QKV, MLP) with per tensor scales (thus N - # scales being passed to the kernel), we convert to the per-channel case. - def process_weights_after_loading(self, layer: torch.nn.Module) -> None: - if (self.strategy == QuantizationStrategy.TENSOR - and len(self.logical_widths) > 1): - - # Load the N per-tensor scales into the channelwise buffer. - weight_scale_channel = torch.empty( - (sum(self.logical_widths), 1), - dtype=torch.float32, - device=layer.weight_scale.device) - start = 0 - for idx, logical_width in enumerate(self.logical_widths): - end = start + logical_width - weight_scale_channel[start:end, :] = layer.weight_scale[idx] - start = end - - layer.weight_scale = Parameter(weight_scale_channel, - requires_grad=False) - - # transpose weights for cutlass. - weight = layer.weight - layer.weight = Parameter(weight.t(), requires_grad=False) - - def create_weights(self, layer: torch.nn.Module, - output_partition_sizes: List[int], - input_size_per_partition: int, - params_dtype: torch.dtype, weight_loader: Callable, - **kwargs): - self.logical_widths = output_partition_sizes - - # WEIGHT SCALE - shape: Union[Tuple[int], Tuple[int, int]] - if self.strategy == QuantizationStrategy.CHANNEL: - shape = (sum(self.logical_widths), 1) - else: - shape = (len(self.logical_widths), ) - - weight_scale = Parameter(torch.empty(*shape, dtype=torch.float32), - requires_grad=False) - layer.register_parameter("weight_scale", weight_scale) - if self.strategy == QuantizationStrategy.CHANNEL: - set_weight_attrs(weight_scale, { - "weight_loader": weight_loader, - "output_dim": 0, - }) - else: - set_weight_attrs(weight_scale, { - "weight_loader": weight_loader, - "needs_scalar_to_array": True, - }) - - # WEIGHT - weight = Parameter(torch.empty(sum(output_partition_sizes), - input_size_per_partition, - dtype=torch.int8), - requires_grad=False) - layer.register_parameter("weight", weight) - set_weight_attrs(weight, { - "input_dim": 1, - "output_dim": 0, - "weight_loader": weight_loader, - }) - - # INPUT SCALE - # Static quantization: load from disk. - if self.is_static_input_scheme: - input_scale = Parameter(torch.empty(1, dtype=torch.float32), - requires_grad=False) - layer.register_parameter("input_scale", input_scale) - set_weight_attrs(input_scale, { - "weight_loader": weight_loader, - "ignore_warning": True, - }) - # Dynamic quantization: set to None. - else: - layer.input_scale = None - - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - # ops.scaled_int8_quant supports both dynamic and static quant. - # * dynamic, layer.input_scale is None and x_scale computed from x. - # * static, layer.input_scale is scalar and x_scale is input_scale. - x_q, x_scale = ops.scaled_int8_quant(x, layer.input_scale) - - return ops.cutlass_scaled_mm(x_q, - layer.weight, - scale_a=x_scale, - scale_b=layer.weight_scale, - out_dtype=x.dtype) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index bcd05a9587d94..b6d50f9beb0af 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -5,8 +5,8 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( - create_per_tensor_scale_param, cutlass_fp8_supported, - apply_fp8_linear, requantize_with_max_scale) + create_per_tensor_scale_param, cutlass_fp8_supported, apply_fp8_linear, + requantize_with_max_scale) from vllm.model_executor.utils import set_weight_attrs __all__ = ["CompressedTensorsW8A8Fp8"] @@ -78,9 +78,10 @@ def apply_weights(self, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - return apply_fp8_linear(input=x, - weight=layer.weight, - weight_scale=layer.weight_scale, - input_scale=layer.input_scale, - bias=bias, - cutlass_fp8_supported=self.cutlass_fp8_supported) + return apply_fp8_linear( + input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale, + bias=bias, + cutlass_fp8_supported=self.cutlass_fp8_supported) diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index 61cfda1e6f94a..fd97b46f96794 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -13,10 +13,10 @@ QuantizationConfig, QuantizeMethodBase) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( all_close_1d, create_per_tensor_scale_param, cutlass_fp8_supported, - apply_fp8_linear, per_tensor_dequantize, per_tensor_quantize, - requantize_with_max_scale) + apply_fp8_linear, per_tensor_dequantize, requantize_with_max_scale) from vllm.model_executor.layers.quantization.utils.marlin_utils import ( - prepare_fp8_layer_for_marlin, apply_fp8_marlin_linear, + prepare_fp8_layer_for_marlin, + apply_fp8_marlin_linear, ) from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform @@ -189,7 +189,7 @@ def process_weights_after_loading(self, layer: Module) -> None: requires_grad=False) else: layer.input_scale = None - + if self.use_marlin: prepare_fp8_layer_for_marlin(layer) # Activations not quantized for marlin. @@ -199,7 +199,7 @@ def apply(self, layer: torch.nn.Module, x: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor: - + if self.use_marlin: return apply_fp8_marlin_linear( input=x, @@ -366,7 +366,7 @@ def process_weights_after_loading(self, layer: Module) -> None: shard_size, :], layer.w13_scale[expert_id][shard_id]) layer.w13_weight[expert_id][ - start:start + shard_size, :] = per_tensor_quantize( + start:start + shard_size, :], _ = ops.scaled_fp8_quant( dq_weight, max_w13_scales[expert_id]) start += shard_size diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 075a87bc5b294..67aa6eeaed102 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -21,36 +21,12 @@ logger = init_logger(__name__) -# Permutations for Marlin scale shuffling -def get_scale_perms(num_bits: int): - scale_perm: List[int] = [] - for i in range(8): - scale_perm.extend([i + 8 * j for j in range(8)]) - scale_perm_single: List[int] = [] - for i in range(4): - scale_perm_single.extend( - [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) - return scale_perm, scale_perm_single - - def get_pack_factor(num_bits: int): assert (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS ), f"Unsupported num_bits = {num_bits}" return 32 // num_bits -def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, - group_size: int, num_bits: int): - scale_perm, scale_perm_single = get_scale_perms(num_bits) - if group_size < size_k and group_size != -1: - s = s.reshape((-1, len(scale_perm)))[:, scale_perm] - else: - s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single] - s = s.reshape((-1, size_n)).contiguous() - - return s - - class GPTQMarlinConfig(QuantizationConfig): """Config class for GPTQ Marlin""" diff --git a/vllm/model_executor/layers/quantization/utils/marlin_utils.py b/vllm/model_executor/layers/quantization/utils/marlin_utils.py index d2b8038602cc4..fcbfd28d31adf 100644 --- a/vllm/model_executor/layers/quantization/utils/marlin_utils.py +++ b/vllm/model_executor/layers/quantization/utils/marlin_utils.py @@ -2,7 +2,7 @@ import random import numpy import torch -from typing import Optional +from typing import List, Optional from vllm.model_executor.layers.quantization.utils.format_24 import ( mask_creator, sparse_semi_structured_from_dense_cutlass) @@ -46,14 +46,16 @@ def apply_fp8_marlin_linear( reshaped_x = input.reshape(-1, input.shape[-1]) out_shape = input.shape[:-1] + (size_n, ) - output = ops.fp8_marlin_gemm(a=reshaped_x, - b_q_weight=weight, - b_scales=weight_scale, - workspace=workspace, - num_bits=8, - size_m=reshaped_x.shape[0], - size_n=size_n, - size_k=size_k,) + output = ops.fp8_marlin_gemm( + a=reshaped_x, + b_q_weight=weight, + b_scales=weight_scale, + workspace=workspace, + num_bits=8, + size_m=reshaped_x.shape[0], + size_n=size_n, + size_k=size_k, + ) if bias is not None: output.add_(bias) # In-place add @@ -148,8 +150,21 @@ def marlin_weights(q_w, size_k, size_n, num_bits, perm): return q_packed -def marlin_permute_scales(s, size_k, size_n, group_size, scale_perm, - scale_perm_single): +# Permutations for Marlin scale shuffling +def get_scale_perms(num_bits: int): + scale_perm: List[int] = [] + for i in range(8): + scale_perm.extend([i + 8 * j for j in range(8)]) + scale_perm_single: List[int] = [] + for i in range(4): + scale_perm_single.extend( + [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) + return scale_perm, scale_perm_single + + +def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, + group_size: int, num_bits: int): + scale_perm, scale_perm_single = get_scale_perms(num_bits) if group_size < size_k and group_size != -1: s = s.reshape((-1, len(scale_perm)))[:, scale_perm] else: From ccee126f749579c8f6e0094e8d855e923eda50c7 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sat, 6 Jul 2024 14:54:55 +0000 Subject: [PATCH 041/114] stash --- .../schemes/compressed_tensors_w8a8_int8.py | 86 +++++++++++++++++++ 1 file changed, 86 insertions(+) create mode 100644 vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py new file mode 100644 index 0000000000000..7078f28e64016 --- /dev/null +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py @@ -0,0 +1,86 @@ +from typing import Callable, List, Tuple, Union + +import torch +from torch.nn import Parameter + +from vllm import _custom_ops as ops +from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( + CompressedTensorsScheme) +from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( + QuantizationStrategy) +from vllm.model_executor.utils import set_weight_attrs +from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( + apply_int8_linear, convert_to_channelwise, + create_per_channel_scale_param, create_per_tensor_scale_param) + + +class CompressedTensorsW8A8Int8(CompressedTensorsScheme): + + def __init__(self, strategy: str, is_static_input_scheme: bool): + self.strategy = strategy + self.is_static_input_scheme = is_static_input_scheme + + def process_weights_after_loading(self, layer: torch.nn.Module) -> None: + # WEIGHT + # Cutlass kernels need transposed weight. + weight = layer.weight + layer.weight = Parameter(weight.t(), requires_grad=False) + + # WEIGHT SCALE + # Cutlass kernels support only per-tensor and per-channel. + # If we have a fused module (QKV, MLP) with per tensor scales (thus N + # scales being passed to the kernel), convert to the per-channel case. + is_fused_module = len(self.logical_widths) > 1 + if is_fused_module and self.strategy == QuantizationStrategy.TENSOR: + ws_channelwise = convert_to_channelwise(layer.weight_scale, + self.logical_widths) + layer.weight_scale = Parameter(ws_channelwise, requires_grad=False) + + # INPUT SCALE + if self.is_static_input_scheme: + layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False) + else: + layer.input_scale = None + + def create_weights(self, layer: torch.nn.Module, + output_partition_sizes: List[int], + input_size_per_partition: int, + params_dtype: torch.dtype, + weight_loader: Callable, + **kwargs): + self.logical_widths = output_partition_sizes + + # WEIGHT + weight = Parameter(torch.empty(sum(output_partition_sizes), + input_size_per_partition, + dtype=torch.int8), + requires_grad=False) + layer.register_parameter("weight", weight) + set_weight_attrs(weight, { + "input_dim": 1, "output_dim": 0, + "weight_loader": weight_loader,}) + + # WEIGHT SCALE + layer_kwargs = {"weight_loader": weight_loader} + if self.strategy == QuantizationStrategy.CHANNEL: + scale = create_per_channel_scale_param(output_partition_sizes, + **layer_kwargs) + else: + assert self.strategy == QuantizationStrategy.TENSOR + scale = create_per_tensor_scale_param(output_partition_sizes, + **layer_kwargs) + layer.register_parameter("weight_scale", scale) + + # INPUT SCALE + if self.is_static_input_scheme: + scale = create_per_tensor_scale_param(output_partition_sizes, + **layer_kwargs) + layer.register_parameter("input_scale", scale) + + + def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): + return apply_int8_linear( + input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale) From 0969c673f2bafa3ee9b6ca720aac44ccd850f0b7 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sat, 6 Jul 2024 14:59:25 +0000 Subject: [PATCH 042/114] format --- .../schemes/compressed_tensors_w8a8_int8.py | 30 +++++++++---------- .../layers/quantization/gptq_marlin.py | 24 +++++++++++++++ .../layers/quantization/utils/marlin_utils.py | 17 ++--------- 3 files changed, 41 insertions(+), 30 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py index 7078f28e64016..67ec2c442bed7 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py @@ -10,8 +10,8 @@ QuantizationStrategy) from vllm.model_executor.utils import set_weight_attrs from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( - apply_int8_linear, convert_to_channelwise, - create_per_channel_scale_param, create_per_tensor_scale_param) + apply_int8_linear, convert_to_channelwise, create_per_channel_scale_param, + create_per_tensor_scale_param) class CompressedTensorsW8A8Int8(CompressedTensorsScheme): @@ -19,13 +19,13 @@ class CompressedTensorsW8A8Int8(CompressedTensorsScheme): def __init__(self, strategy: str, is_static_input_scheme: bool): self.strategy = strategy self.is_static_input_scheme = is_static_input_scheme - + def process_weights_after_loading(self, layer: torch.nn.Module) -> None: # WEIGHT # Cutlass kernels need transposed weight. weight = layer.weight layer.weight = Parameter(weight.t(), requires_grad=False) - + # WEIGHT SCALE # Cutlass kernels support only per-tensor and per-channel. # If we have a fused module (QKV, MLP) with per tensor scales (thus N @@ -38,15 +38,15 @@ def process_weights_after_loading(self, layer: torch.nn.Module) -> None: # INPUT SCALE if self.is_static_input_scheme: - layer.input_scale = Parameter(layer.input_scale.max(), requires_grad=False) + layer.input_scale = Parameter(layer.input_scale.max(), + requires_grad=False) else: layer.input_scale = None def create_weights(self, layer: torch.nn.Module, output_partition_sizes: List[int], input_size_per_partition: int, - params_dtype: torch.dtype, - weight_loader: Callable, + params_dtype: torch.dtype, weight_loader: Callable, **kwargs): self.logical_widths = output_partition_sizes @@ -57,8 +57,10 @@ def create_weights(self, layer: torch.nn.Module, requires_grad=False) layer.register_parameter("weight", weight) set_weight_attrs(weight, { - "input_dim": 1, "output_dim": 0, - "weight_loader": weight_loader,}) + "input_dim": 1, + "output_dim": 0, + "weight_loader": weight_loader, + }) # WEIGHT SCALE layer_kwargs = {"weight_loader": weight_loader} @@ -76,11 +78,9 @@ def create_weights(self, layer: torch.nn.Module, scale = create_per_tensor_scale_param(output_partition_sizes, **layer_kwargs) layer.register_parameter("input_scale", scale) - def apply_weights(self, layer: torch.nn.Module, x: torch.Tensor): - return apply_int8_linear( - input=x, - weight=layer.weight, - weight_scale=layer.weight_scale, - input_scale=layer.input_scale) + return apply_int8_linear(input=x, + weight=layer.weight, + weight_scale=layer.weight_scale, + input_scale=layer.input_scale) diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 67aa6eeaed102..075a87bc5b294 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -21,12 +21,36 @@ logger = init_logger(__name__) +# Permutations for Marlin scale shuffling +def get_scale_perms(num_bits: int): + scale_perm: List[int] = [] + for i in range(8): + scale_perm.extend([i + 8 * j for j in range(8)]) + scale_perm_single: List[int] = [] + for i in range(4): + scale_perm_single.extend( + [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) + return scale_perm, scale_perm_single + + def get_pack_factor(num_bits: int): assert (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS ), f"Unsupported num_bits = {num_bits}" return 32 // num_bits +def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, + group_size: int, num_bits: int): + scale_perm, scale_perm_single = get_scale_perms(num_bits) + if group_size < size_k and group_size != -1: + s = s.reshape((-1, len(scale_perm)))[:, scale_perm] + else: + s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single] + s = s.reshape((-1, size_n)).contiguous() + + return s + + class GPTQMarlinConfig(QuantizationConfig): """Config class for GPTQ Marlin""" diff --git a/vllm/model_executor/layers/quantization/utils/marlin_utils.py b/vllm/model_executor/layers/quantization/utils/marlin_utils.py index fcbfd28d31adf..af3940635cc5b 100644 --- a/vllm/model_executor/layers/quantization/utils/marlin_utils.py +++ b/vllm/model_executor/layers/quantization/utils/marlin_utils.py @@ -150,21 +150,8 @@ def marlin_weights(q_w, size_k, size_n, num_bits, perm): return q_packed -# Permutations for Marlin scale shuffling -def get_scale_perms(num_bits: int): - scale_perm: List[int] = [] - for i in range(8): - scale_perm.extend([i + 8 * j for j in range(8)]) - scale_perm_single: List[int] = [] - for i in range(4): - scale_perm_single.extend( - [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) - return scale_perm, scale_perm_single - - -def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, - group_size: int, num_bits: int): - scale_perm, scale_perm_single = get_scale_perms(num_bits) +def marlin_permute_scales(s, size_k, size_n, group_size, scale_perm, + scale_perm_single): if group_size < size_k and group_size != -1: s = s.reshape((-1, len(scale_perm)))[:, scale_perm] else: From b2eeb84b91e591ea8cac9d2a441d0fc79917b494 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sat, 6 Jul 2024 15:01:12 +0000 Subject: [PATCH 043/114] fix imported marlin_permute_scales --- vllm/model_executor/layers/quantization/utils/marlin_utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/vllm/model_executor/layers/quantization/utils/marlin_utils.py b/vllm/model_executor/layers/quantization/utils/marlin_utils.py index af3940635cc5b..9d632027b5453 100644 --- a/vllm/model_executor/layers/quantization/utils/marlin_utils.py +++ b/vllm/model_executor/layers/quantization/utils/marlin_utils.py @@ -2,7 +2,7 @@ import random import numpy import torch -from typing import List, Optional +from typing import Optional from vllm.model_executor.layers.quantization.utils.format_24 import ( mask_creator, sparse_semi_structured_from_dense_cutlass) From 9316f92080ee8e05d4820e967405c7f2c76a1d0e Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sun, 7 Jul 2024 14:00:26 +0000 Subject: [PATCH 044/114] format --- .../compressed_tensors/compressed_tensors.py | 2 +- .../compressed_tensors/schemes/__init__.py | 10 +++++----- .../schemes/compressed_tensors_w8a8_fp8.py | 2 +- .../schemes/compressed_tensors_w8a8_int8.py | 5 ++--- vllm/model_executor/layers/quantization/fp8.py | 10 ++++------ vllm/model_executor/layers/quantization/gptq_marlin.py | 9 +++++---- .../layers/quantization/utils/marlin_utils.py | 10 ++++++---- 7 files changed, 24 insertions(+), 24 deletions(-) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py index 632006af27baf..c711fd14c668c 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/compressed_tensors.py @@ -9,7 +9,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( W4A16SPARSE24_SUPPORTED_BITS, WNA16_SUPPORTED_BITS, CompressedTensorsScheme, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8Int8, CompressedTensorsW8A8Fp8, + CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( CompressionFormat, QuantizationArgs, QuantizationStrategy, diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py index 9d47b80017a59..dd94c49827f62 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/__init__.py @@ -1,11 +1,11 @@ from .compressed_tensors_scheme import CompressedTensorsScheme from .compressed_tensors_unquantized import CompressedTensorsUnquantized -from .compressed_tensors_w4a16_24 import (CompressedTensorsW4A16Sparse24, - W4A16SPARSE24_SUPPORTED_BITS) -from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8 +from .compressed_tensors_w4a16_24 import (W4A16SPARSE24_SUPPORTED_BITS, + CompressedTensorsW4A16Sparse24) from .compressed_tensors_w8a8_fp8 import CompressedTensorsW8A8Fp8 -from .compressed_tensors_wNa16 import (CompressedTensorsWNA16, - WNA16_SUPPORTED_BITS) +from .compressed_tensors_w8a8_int8 import CompressedTensorsW8A8Int8 +from .compressed_tensors_wNa16 import (WNA16_SUPPORTED_BITS, + CompressedTensorsWNA16) __all__ = [ "CompressedTensorsScheme", diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py index b6d50f9beb0af..b93425fb2d629 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_fp8.py @@ -5,7 +5,7 @@ from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( - create_per_tensor_scale_param, cutlass_fp8_supported, apply_fp8_linear, + apply_fp8_linear, create_per_tensor_scale_param, cutlass_fp8_supported, requantize_with_max_scale) from vllm.model_executor.utils import set_weight_attrs diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py index 67ec2c442bed7..e70504ec51cb3 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_w8a8_int8.py @@ -1,17 +1,16 @@ -from typing import Callable, List, Tuple, Union +from typing import Callable, List import torch from torch.nn import Parameter -from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( QuantizationStrategy) -from vllm.model_executor.utils import set_weight_attrs from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( apply_int8_linear, convert_to_channelwise, create_per_channel_scale_param, create_per_tensor_scale_param) +from vllm.model_executor.utils import set_weight_attrs class CompressedTensorsW8A8Int8(CompressedTensorsScheme): diff --git a/vllm/model_executor/layers/quantization/fp8.py b/vllm/model_executor/layers/quantization/fp8.py index fd97b46f96794..8dba9019f94cf 100644 --- a/vllm/model_executor/layers/quantization/fp8.py +++ b/vllm/model_executor/layers/quantization/fp8.py @@ -11,13 +11,11 @@ from vllm.model_executor.layers.linear import LinearBase, LinearMethodBase from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig, QuantizeMethodBase) -from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( - all_close_1d, create_per_tensor_scale_param, cutlass_fp8_supported, - apply_fp8_linear, per_tensor_dequantize, requantize_with_max_scale) from vllm.model_executor.layers.quantization.utils.marlin_utils import ( - prepare_fp8_layer_for_marlin, - apply_fp8_marlin_linear, -) + apply_fp8_marlin_linear, prepare_fp8_layer_for_marlin) +from vllm.model_executor.layers.quantization.utils.w8a8_utils import ( + all_close_1d, apply_fp8_linear, create_per_tensor_scale_param, + cutlass_fp8_supported, per_tensor_dequantize, requantize_with_max_scale) from vllm.model_executor.utils import set_weight_attrs from vllm.platforms import current_platform from vllm.utils import print_warning_once diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 075a87bc5b294..6b971f73d45bf 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -11,12 +11,13 @@ set_weight_attrs) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) +from vllm.model_executor.layers.quantization.utils.marlin_utils import ( + GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_K, + GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, + GPTQ_MARLIN_SUPPORTED_NUM_BITS, GPTQ_MARLIN_SUPPORTED_SYM, + GPTQ_MARLIN_TILE) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead from vllm.platforms import current_platform -from vllm.model_executor.layers.quantization.utils.marlin_utils import ( - GPTQ_MARLIN_TILE, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MIN_THREAD_K, - GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_SUPPORTED_NUM_BITS, - GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_SYM) logger = init_logger(__name__) diff --git a/vllm/model_executor/layers/quantization/utils/marlin_utils.py b/vllm/model_executor/layers/quantization/utils/marlin_utils.py index 9d632027b5453..9886245269ad3 100644 --- a/vllm/model_executor/layers/quantization/utils/marlin_utils.py +++ b/vllm/model_executor/layers/quantization/utils/marlin_utils.py @@ -1,9 +1,11 @@ """This file is used for /tests and /benchmarks""" import random +from typing import Optional + import numpy import torch -from typing import Optional +from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.utils.format_24 import ( mask_creator, sparse_semi_structured_from_dense_cutlass) from vllm.model_executor.layers.quantization.utils.marlin_24_perms import ( @@ -13,7 +15,6 @@ from vllm.model_executor.layers.quantization.utils.quant_utils import ( get_pack_factor, quantize_weights, sort_weights) from vllm.platforms import current_platform -from vllm import _custom_ops as ops from vllm.utils import print_warning_once GPTQ_MARLIN_TILE = 16 @@ -95,13 +96,14 @@ def prepare_fp8_layer_for_marlin(layer: torch.nn.Module) -> None: scales = layer.weight_scale.repeat(1, part_size_n).to( layer.orig_dtype).to(device) # Permute scales + num_bits = 8 marlin_scales = marlin_permute_scales( s=scales, size_k=part_size_k, size_n=part_size_n, group_size=-1, - num_bits=8, - ) + scale_perm=marlin_scale_perm[num_bits], + scale_perm_single=marlin_scale_perm_single[num_bits]) layer.weight_scale = torch.nn.Parameter(marlin_scales, requires_grad=False) # Allocate marlin workspace From 4ff23c8de30ee0a0c2838ae5a68857bb3f8ab1e0 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sun, 7 Jul 2024 14:16:31 +0000 Subject: [PATCH 045/114] added w8a8 to correctness testing --- ...3-8B-Instruct-INT8-compressed-tensors.yaml | 11 ++++++++++ .../lm-eval-harness/configs/models-small.txt | 1 + tests/quantization/test_compressed_tensors.py | 22 ++++++++++++++++--- 3 files changed, 31 insertions(+), 3 deletions(-) create mode 100644 .buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml diff --git a/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml new file mode 100644 index 0000000000000..38f04f2617128 --- /dev/null +++ b/.buildkite/lm-eval-harness/configs/Meta-Llama-3-8B-Instruct-INT8-compressed-tensors.yaml @@ -0,0 +1,11 @@ +# bash .buildkite/lm-eval-harness/run-lm-eval-gsm-vllm-baseline.sh -m nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test -b "auto" -l 250 -f 5 -t 1 +model_name: "nm-testing/Meta-Llama-3-8B-Instruct-W8-Channel-A8-Dynamic-Per-Token-Test" +tasks: +- name: "gsm8k" + metrics: + - name: "exact_match,strict-match" + value: 0.744 + - name: "exact_match,flexible-extract" + value: 0.744 +limit: 250 +num_fewshot: 5 diff --git a/.buildkite/lm-eval-harness/configs/models-small.txt b/.buildkite/lm-eval-harness/configs/models-small.txt index fc3f0144f8a04..90d60dd2b9a35 100644 --- a/.buildkite/lm-eval-harness/configs/models-small.txt +++ b/.buildkite/lm-eval-harness/configs/models-small.txt @@ -1,3 +1,4 @@ Meta-Llama-3-8B-Instruct.yaml Meta-Llama-3-8B-Instruct-FP8.yaml Meta-Llama-3-8B-FP8-compressed-tensors.yaml +Meta-Llama-3-8B-INT8-compressed-tensors.yaml diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index 426d4b51e5759..e18292ab5545c 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -9,7 +9,7 @@ from vllm import SamplingParams from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import ( # noqa: E501 CompressedTensorsLinearMethod, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8Int8, CompressedTensorsWNA16) + CompressedTensorsW8A8Int8, CompressedTensorsW8A8Fp8, CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( QuantizationType) @@ -41,8 +41,7 @@ def test_compressed_tensors_w8a8_static_setup(vllm_runner, model_args): assert qkv_proj.scheme.strategy == strategy assert qkv_proj.scheme.is_static_input_scheme - expected_type = (torch.int8 if quant_type == QuantizationType.INT else - torch.float8_e4m3fn) + expected_type = torch.int8 assert qkv_proj.weight.dtype is expected_type assert o_proj.weight.dtype is expected_type @@ -123,3 +122,20 @@ def test_compressed_tensors_w4a16_marlin24(vllm_runner): sampling_params = SamplingParams() output = llm.generate("Hello world!", sampling_params=sampling_params) assert output + +def test_compressed_tensors_fp8(vllm_runner): + model_path = "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test" + with vllm_runner(model_path) as llm: + model = llm.model.llm_engine.model_executor.driver_worker.model_runner.model # noqa: E501 + layer = model.model.layers[0] + + qkv_proj = layer.self_attn.qkv_proj + + assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) + assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8) + assert qkv_proj.weight.dtype is torch.int32 + + sampling_params = SamplingParams() + output = llm.generate("Hello world!", sampling_params=sampling_params) + assert output + \ No newline at end of file From 08a8e4e6b57c388d25584aa403a5563f0d413ef9 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sun, 7 Jul 2024 14:19:15 +0000 Subject: [PATCH 046/114] added testing --- tests/quantization/test_compressed_tensors.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index e18292ab5545c..87aaef80e30f1 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -133,7 +133,10 @@ def test_compressed_tensors_fp8(vllm_runner): assert isinstance(qkv_proj.quant_method, CompressedTensorsLinearMethod) assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8) - assert qkv_proj.weight.dtype is torch.int32 + assert qkv_proj.weight.dtype is torch.float8_e4m3fn + assert qkv_proj.input_scale.dtype is torch.float32 + assert qkv_proj.input_scale.shape[0] == 1 + assert qkv_proj.weight_scale.shape[0] == 1 sampling_params = SamplingParams() output = llm.generate("Hello world!", sampling_params=sampling_params) From 4238ac9224b3fb5482ac29a5da64d0644703dccf Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sun, 7 Jul 2024 14:22:51 +0000 Subject: [PATCH 047/114] format --- tests/quantization/test_compressed_tensors.py | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/tests/quantization/test_compressed_tensors.py b/tests/quantization/test_compressed_tensors.py index 87aaef80e30f1..96223a247657b 100644 --- a/tests/quantization/test_compressed_tensors.py +++ b/tests/quantization/test_compressed_tensors.py @@ -9,7 +9,8 @@ from vllm import SamplingParams from vllm.model_executor.layers.quantization.compressed_tensors.compressed_tensors import ( # noqa: E501 CompressedTensorsLinearMethod, CompressedTensorsW4A16Sparse24, - CompressedTensorsW8A8Int8, CompressedTensorsW8A8Fp8, CompressedTensorsWNA16) + CompressedTensorsW8A8Fp8, CompressedTensorsW8A8Int8, + CompressedTensorsWNA16) from vllm.model_executor.layers.quantization.compressed_tensors.utils import ( QuantizationType) @@ -123,6 +124,7 @@ def test_compressed_tensors_w4a16_marlin24(vllm_runner): output = llm.generate("Hello world!", sampling_params=sampling_params) assert output + def test_compressed_tensors_fp8(vllm_runner): model_path = "nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test" with vllm_runner(model_path) as llm: @@ -135,10 +137,11 @@ def test_compressed_tensors_fp8(vllm_runner): assert isinstance(qkv_proj.scheme, CompressedTensorsW8A8Fp8) assert qkv_proj.weight.dtype is torch.float8_e4m3fn assert qkv_proj.input_scale.dtype is torch.float32 - assert qkv_proj.input_scale.shape[0] == 1 - assert qkv_proj.weight_scale.shape[0] == 1 + assert qkv_proj.weight_scale.dtype is torch.float32 + # should be scalars after processing + assert len(qkv_proj.input_scale.shape) == 0 + assert len(qkv_proj.weight_scale.shape) == 0 sampling_params = SamplingParams() output = llm.generate("Hello world!", sampling_params=sampling_params) assert output - \ No newline at end of file From 94d6b35ee5cd1ced575114607ff6ce6d35e319e6 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sun, 7 Jul 2024 14:55:22 +0000 Subject: [PATCH 048/114] stash --- tests/kernels/test_marlin_gemm.py | 12 +- .../schemes/compressed_tensors_wNa16.py | 2 +- .../layers/quantization/gptq_marlin.py | 108 ++++---------- .../layers/quantization/utils/marlin.py | 134 ------------------ 4 files changed, 30 insertions(+), 226 deletions(-) delete mode 100644 vllm/model_executor/layers/quantization/utils/marlin.py diff --git a/tests/kernels/test_marlin_gemm.py b/tests/kernels/test_marlin_gemm.py index 022d0ee9c70f8..d2891ae9c79b1 100644 --- a/tests/kernels/test_marlin_gemm.py +++ b/tests/kernels/test_marlin_gemm.py @@ -6,10 +6,6 @@ import torch from vllm import _custom_ops as ops -from vllm.model_executor.layers.quantization.utils.marlin import ( - GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, - GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS, - marlin_permute_scales) from vllm.model_executor.layers.quantization.gptq_marlin_24 import ( GPTQ_MARLIN_24_MAX_PARALLEL, GPTQ_MARLIN_24_MIN_THREAD_N, GPTQ_MARLIN_24_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_24_SUPPORTED_NUM_BITS) @@ -18,6 +14,10 @@ from vllm.model_executor.layers.quantization.test_utils.marlin_utils import ( MarlinWorkspace, compute_max_diff, is_marlin_supported, marlin_24_quantize, marlin_quantize, marlin_weights, pack_fp8_to_int32) +from vllm.model_executor.layers.quantization.utils.marlin_utils import ( + GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, + GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, GPTQ_MARLIN_SUPPORTED_NUM_BITS, + marlin_permute_scales) from vllm.model_executor.layers.quantization.utils.quant_utils import ( gptq_pack, quantize_weights, sort_weights) @@ -272,9 +272,7 @@ def test_fp8_marlin_gemm( s=scales, size_k=size_k, size_n=size_n, - group_size=-1, - num_bits=8, - ) + group_size=-1) workspace = MarlinWorkspace(size_n, GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_MAX_PARALLEL) diff --git a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py index 76fb9fc14ce90..32312e0ffa251 100644 --- a/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py +++ b/vllm/model_executor/layers/quantization/compressed_tensors/schemes/compressed_tensors_wNa16.py @@ -6,7 +6,7 @@ from vllm import _custom_ops as ops from vllm.model_executor.layers.quantization.compressed_tensors.schemes import ( CompressedTensorsScheme) -from vllm.model_executor.layers.quantization.utils.marlin import ( +from vllm.model_executor.layers.quantization.utils.marlin_utils import ( GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_N, GPTQMarlinState, marlin_permute_scales) from vllm.model_executor.utils import set_weight_attrs diff --git a/vllm/model_executor/layers/quantization/gptq_marlin.py b/vllm/model_executor/layers/quantization/gptq_marlin.py index 6fcce254d561e..2610d21001569 100644 --- a/vllm/model_executor/layers/quantization/gptq_marlin.py +++ b/vllm/model_executor/layers/quantization/gptq_marlin.py @@ -1,7 +1,8 @@ +import enum +from enum import Enum from typing import Any, Dict, List, Optional import torch -from torch.nn.modules import Module from torch.nn.parameter import Parameter from vllm import _custom_ops as ops @@ -10,58 +11,15 @@ set_weight_attrs) from vllm.model_executor.layers.quantization.base_config import ( QuantizationConfig) -<<<<<<< HEAD -from vllm.model_executor.layers.quantization.utils.marlin import ( - check_marlin_supported, get_max_workspace_size, marlin_permute_scales, - replace_tensor, sort_g_idx, verify_marlin_supported, - verify_marlin_supports_shape) -from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead - -logger = init_logger(__name__) - -======= from vllm.model_executor.layers.quantization.utils.marlin_utils import ( - GPTQ_MARLIN_MAX_PARALLEL, GPTQ_MARLIN_MIN_THREAD_K, - GPTQ_MARLIN_MIN_THREAD_N, GPTQ_MARLIN_SUPPORTED_GROUP_SIZES, - GPTQ_MARLIN_SUPPORTED_NUM_BITS, GPTQ_MARLIN_SUPPORTED_SYM, - GPTQ_MARLIN_TILE) + verify_marlin_supports_shape, verify_marlin_supported, check_marlin_supported, + get_max_workspace_size, marlin_permute_scales, marlin_sort_g_idx, + replace_tensor) from vllm.model_executor.layers.vocab_parallel_embedding import ParallelLMHead -from vllm.platforms import current_platform logger = init_logger(__name__) -# Permutations for Marlin scale shuffling -def get_scale_perms(num_bits: int): - scale_perm: List[int] = [] - for i in range(8): - scale_perm.extend([i + 8 * j for j in range(8)]) - scale_perm_single: List[int] = [] - for i in range(4): - scale_perm_single.extend( - [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) - return scale_perm, scale_perm_single - - -def get_pack_factor(num_bits: int): - assert (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS - ), f"Unsupported num_bits = {num_bits}" - return 32 // num_bits - - -def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, - group_size: int, num_bits: int): - scale_perm, scale_perm_single = get_scale_perms(num_bits) - if group_size < size_k and group_size != -1: - s = s.reshape((-1, len(scale_perm)))[:, scale_perm] - else: - s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single] - s = s.reshape((-1, size_n)).contiguous() - - return s - ->>>>>>> compressed-tensors-fp8 - class GPTQMarlinConfig(QuantizationConfig): """Config class for GPTQ Marlin""" @@ -79,6 +37,7 @@ def __init__(self, weight_bits: int, group_size: int, desc_act: bool, self.is_sym = is_sym self.lm_head_quantized = lm_head_quantized + # Verify supported on platform. verify_marlin_supported(num_bits=self.weight_bits, group_size=self.group_size, is_sym=self.is_sym) @@ -160,28 +119,10 @@ def is_marlin_compatible(cls, quant_config: Dict[str, Any]): or desc_act is None): return False -<<<<<<< HEAD return check_marlin_supported(num_bits=num_bits, group_size=group_size, is_sym=sym, min_capability=cls.get_min_capability()) -======= - # If the capability of the device is too low, cannot convert. - major, minor = current_platform.get_device_capability() - device_capability = major * 10 + minor - if device_capability < cls.get_min_capability(): - return False - - # Otherwise, can convert if model satisfies marlin constraints. - return (num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS - and group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES - and sym in GPTQ_MARLIN_SUPPORTED_SYM) - - -class GPTQMarlinState(Enum): - REPACK = enum.auto() - READY = enum.auto() ->>>>>>> compressed-tensors-fp8 class GPTQMarlinLinearMethod(LinearMethodBase): @@ -205,7 +146,6 @@ def create_weights( **extra_weight_attrs, ) -> None: del output_size - output_size_per_partition = sum(output_partition_sizes) # Normalize group_size @@ -215,7 +155,7 @@ def create_weights( group_size = input_size verify_marlin_supports_shape( - output_size_per_partition=sum(output_partition_sizes), + output_size_per_partition=output_size_per_partition, input_size_per_partition=input_size_per_partition, input_size=input_size, group_size=group_size) @@ -226,14 +166,13 @@ def create_weights( scales_and_zp_size = input_size // group_size scales_and_zp_input_dim = None + # Act-order case: if self.quant_config.desc_act: - # Act-order case assert self.quant_config.group_size != -1 is_k_full = input_size_per_partition == input_size + # No act-order case: else: - # No act-order case - # K is always full due to full alignment with # group-size and shard of scales/zp is_k_full = True @@ -344,27 +283,28 @@ def create_weights( layer.input_size = input_size layer.is_k_full = is_k_full - def process_weights_after_loading(self, layer: Module) -> None: - + def process_weights_after_loading(self, layer: torch.nn.Module) -> None: + # To be used as part of repacking part_size_n = layer.output_size_per_partition part_size_k = layer.input_size_per_partition full_size_k = layer.input_size - # Sort for act_order if self.quant_config.desc_act: - # Get sorting based on g_idx - sort_g_idx(layer, "g_idx", "g_idx_sort_indices") + # If desc_act, sort for activation_reordering. + g_idx, g_idx_sort_indices = marlin_sort_g_idx( + layer.g_idx, layer.g_idx_sort_indices) + replace_tensor(layer, "g_idx", g_idx) + replace_tensor(layer, "g_idx_sort_indices", g_idx_sort_indices) + else: - # Reset g_idx to empty - device = layer.qweight.device - layer.g_idx = Parameter(torch.empty(0, dtype=torch.int, - device=device), - requires_grad=False) - layer.g_idx_sort_indices = Parameter(torch.empty(0, - dtype=torch.int, - device=device), - requires_grad=False) + # Otherwise, reset g_idx to empty. + layer.g_idx = Parameter( + torch.empty(0, dtype=torch.int, device=layer.qweight.device), + requires_grad=False) + layer.g_idx_sort_indices = Parameter( + torch.empty(0, dtype=torch.int, device=layer.qweight.device), + requires_grad=False) # Repack weights into marlin format marlin_qweight = ops.gptq_marlin_repack( diff --git a/vllm/model_executor/layers/quantization/utils/marlin.py b/vllm/model_executor/layers/quantization/utils/marlin.py deleted file mode 100644 index 6024a58e0f127..0000000000000 --- a/vllm/model_executor/layers/quantization/utils/marlin.py +++ /dev/null @@ -1,134 +0,0 @@ -import enum -from typing import List - -import torch - -from vllm.utils import get_device_capability_stateless - - -class GPTQMarlinState(enum.Enum): - REPACK = enum.auto() - READY = enum.auto() - - -GPTQ_MARLIN_TILE = 16 -GPTQ_MARLIN_MIN_THREAD_N = 64 -GPTQ_MARLIN_MIN_THREAD_K = 128 -GPTQ_MARLIN_MAX_PARALLEL = 16 - -GPTQ_MARLIN_SUPPORTED_NUM_BITS = [4, 8] -GPTQ_MARLIN_SUPPORTED_GROUP_SIZES = [-1, 32, 64, 128] -GPTQ_MARLIN_SUPPORTED_SYM = [True] -GTPQ_MARLIN_UNSUPPORTED_GROUP_SIZE_ACT_ORDER = [-1] - - -# Permutations for Marlin scale shuffling -def get_scale_perms(num_bits: int): - scale_perm: List[int] = [] - for i in range(8): - scale_perm.extend([i + 8 * j for j in range(8)]) - scale_perm_single: List[int] = [] - for i in range(4): - scale_perm_single.extend( - [2 * i + j for j in [0, 1, 8, 9, 16, 17, 24, 25]]) - return scale_perm, scale_perm_single - - -def marlin_permute_scales(s: torch.Tensor, size_k: int, size_n: int, - group_size: int, num_bits: int): - scale_perm, scale_perm_single = get_scale_perms(num_bits) - if group_size < size_k and group_size != -1: - s = s.reshape((-1, len(scale_perm)))[:, scale_perm] - else: - s = s.reshape((-1, len(scale_perm_single)))[:, scale_perm_single] - s = s.reshape((-1, size_n)).contiguous() - - return s - - -def check_marlin_supported(num_bits: int, group_size: int, is_sym: bool, - min_capability: int) -> bool: - - # If the capability of the device is too low, cannot convert. - major, minor = get_device_capability_stateless() - device_capability = major * 10 + minor - if device_capability < min_capability: - return False - - return (device_capability >= min_capability - and num_bits in GPTQ_MARLIN_SUPPORTED_NUM_BITS - and group_size in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES - and is_sym in GPTQ_MARLIN_SUPPORTED_SYM) - - -def verify_marlin_supported(num_bits: int, group_size: int, - is_sym: bool) -> None: - - if num_bits not in GPTQ_MARLIN_SUPPORTED_NUM_BITS: - raise ValueError( - f"Marlin does not support weight_bits = {num_bits}. " - f"Only weight_bits = {GPTQ_MARLIN_SUPPORTED_NUM_BITS} " - "are supported.") - if group_size not in GPTQ_MARLIN_SUPPORTED_GROUP_SIZES: - raise ValueError( - f"Marlin does not support group_size = {group_size}. " - f"Only group_sizes = {GPTQ_MARLIN_SUPPORTED_GROUP_SIZES} " - "are supported.") - if is_sym not in GPTQ_MARLIN_SUPPORTED_SYM: - raise ValueError( - f"Marlin does not support is_sym = is_sym. " - f"Only sym = {GPTQ_MARLIN_SUPPORTED_SYM} are supported.") - - -def verify_marlin_supports_shape(output_size_per_partition: int, - input_size_per_partition: int, - input_size: int, group_size: int) -> None: - - # Validate output_size_per_partition - if output_size_per_partition % GPTQ_MARLIN_MIN_THREAD_N != 0: - raise ValueError(f"Weight output_size_per_partition = " - f"{output_size_per_partition} is not divisible by " - f" min_thread_n = {GPTQ_MARLIN_MIN_THREAD_N}. " - "Consider reducing tensor_parallel_size or running " - "with --quantization gptq.") - - # Validate input_size_per_partition - if input_size_per_partition % GPTQ_MARLIN_MIN_THREAD_K != 0: - raise ValueError(f"Weight input_size_per_partition = " - f"{input_size_per_partition} is not divisible " - f"by min_thread_k = {GPTQ_MARLIN_MIN_THREAD_K}. " - "Consider reducing tensor_parallel_size or running " - "with --quantization gptq.") - - if (group_size < input_size - and input_size_per_partition % group_size != 0): - raise ValueError( - f"Weight input_size_per_partition = {input_size_per_partition}" - f" is not divisible by group_size = {group_size}." - "Consider reducing tensor_parallel_size or running " - "with --quantization gptq.") - - -def get_max_workspace_size(output_size_per_partition: int): - return (output_size_per_partition // - GPTQ_MARLIN_MIN_THREAD_N) * GPTQ_MARLIN_MAX_PARALLEL - - -# Newly generated tensors need to replace existing tensors that are -# already registered as parameters by vLLM (and won't be freed) -def replace_tensor(layer: torch.nn.Module, name: str, - new_t: torch.Tensor) -> None: - # It is important to use resize_() here since it ensures - # the same buffer is reused - getattr(layer, name).resize_(new_t.shape) - getattr(layer, name).copy_(new_t) - del new_t - - -# Perform softing -def sort_g_idx(layer: torch.nn.Module, g_idx_name: str, - g_idx_sort_indices_name: str): - g_idx = getattr(layer, g_idx_name) - g_idx_sort_indices = torch.argsort(g_idx).to(torch.int) - replace_tensor(layer, g_idx_name, g_idx[g_idx_sort_indices]) - replace_tensor(layer, g_idx_sort_indices_name, g_idx_sort_indices) From d48ba9d0548ce54a27a0775267e8d128f9101806 Mon Sep 17 00:00:00 2001 From: Robert Shaw Date: Sun, 7 Jul 2024 14:56:07 +0000 Subject: [PATCH 049/114] readded --- example.py | 30 ++ lm-eval.sh | 51 ++ lm-evaluation-harness | 1 + nm-vllm-env/bin/Activate.ps1 | 247 ++++++++++ nm-vllm-env/bin/accelerate | 8 + nm-vllm-env/bin/accelerate-config | 8 + nm-vllm-env/bin/accelerate-estimate-memory | 8 + nm-vllm-env/bin/accelerate-launch | 8 + nm-vllm-env/bin/accelerate-merge-weights | 8 + nm-vllm-env/bin/activate | 69 +++ nm-vllm-env/bin/activate.csh | 26 + nm-vllm-env/bin/activate.fish | 69 +++ nm-vllm-env/bin/aws | 27 + nm-vllm-env/bin/aws.cmd | 59 +++ nm-vllm-env/bin/aws_bash_completer 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mode 100755 nm-vllm-env/bin/uvicorn create mode 100755 nm-vllm-env/bin/watchfiles create mode 100755 nm-vllm-env/bin/yapf create mode 100755 nm-vllm-env/bin/yapf-diff create mode 120000 nm-vllm-env/lib64 create mode 100644 nm-vllm-env/pyvenv.cfg create mode 100644 nm-vllm-env/share/man/man1/isympy.1 create mode 100644 nm-vllm-env/share/man/man1/ttx.1 create mode 100644 qwen2_moe_old.py create mode 100644 run-lm-eval.sh create mode 100644 run.py create mode 100644 test.sh create mode 100644 vllm/model_executor/layers/quantization/utils/marlin_utils.py create mode 100644 vllm/model_executor/layers/quantization/utils/w8a8_utils.py diff --git a/example.py b/example.py new file mode 100644 index 0000000000000..cf69eaa936efe --- /dev/null +++ b/example.py @@ -0,0 +1,30 @@ +from vllm import LLM, SamplingParams +from transformers import AutoTokenizer + +# MODEL="nm-testing/Meta-Llama-3-8B-Instruct-W4-Group128-A16-Test" +# MODEL="nm-testing/Meta-Llama-3-8B-FP8-compressed-tensors-test-bos" +# MODEL="neuralmagic/Meta-Llama-3-8B-Instruct-FP8" +# MODEL="nm-testing/Meta-Llama-3-8B-Instruct-GPTQ" +MODEL="llm-compressor/Meta-Llama-3-8B-Instruct-W8A8-FP8-BOS" + +tokenizer = AutoTokenizer.from_pretrained(MODEL) + +PROMPT = "Question: Steve finds 100 gold bars while visiting Oregon. He wants to distribute his gold bars evenly to his 4 friends. If 20 gold bars were lost on the way back to San Diego, how many gold bars will each of his 4 friends get when he returns?\nAnswer: He only has 100 - 20 = <<100-20=80>>80 gold bars after losing 20 of them.\nHe then gives each of his friends 80 ÷ 4 = <<80/4=20>>20 gold bars.\n#### 20\n\nQuestion: In a week, Mortdecai collects 8 dozen eggs every Tuesday and Thursday, and he delivers 3 dozen eggs to the market and 5 dozen eggs to the mall. He then uses 4 dozen eggs to make a pie every Saturday. Mortdecai donates the remaining eggs to the charity by Sunday. How many eggs does he donate to the charity?\nAnswer: Mortdecai collects a total of 8x2 = <<8*2=16>>16 dozens of eggs.\nHe sells a total of 3 + 5 = <<3+5=8>>8 dozens of eggs.\nSo, 16 - 8 = <<16-8=8>>8 dozens of eggs are left.\nAfter using 4 dozens of eggs to make a pie, 8 - 4 = <<8-4=4>>4 dozens of eggs are left.\nSince there are 12 in 1 dozen, then Mortdecai donates 4 x 12 = <<4*12=48>>48 pieces of eggs to the charity.\n#### 48\n\nQuestion: Corey downloaded two movie series from his Netflix account with 12 and 14 seasons per series, respectively. However, in the week, his computer got a mechanical failure, and he lost two episodes from each season for both series. If each season in the movie series that Corey downloaded had 16 episodes, how many episodes remained after the computer's mechanical failure?\nAnswer: In the first movie series with 12 seasons, after the mechanical failure, the number of episodes that Corey lost is 2*12 = <<2*12=24>>24\nOriginally, the movie series with 12 seasons had 12*16 = <<12*16=192>>192 episodes.\nAfter the mechanical failure, Corey had 192-24 = <<192-24=168>>168 episodes remaining in the first movie series.\nSimilarly, the 14 season movie series also had 14*2 = <<14*2=28>>28 lost after the computer's mechanical failure.\nOriginally, the movie series with 14 seasons has 14*16 = <<14*16=224>>224 episodes.\nThe mechanical failure of the computer reduced the number of episodes in the 14 season movie series to 224-28 = <<224-28=196>>196\nAfter the loss, Corey had 196+168 = <<196+168=364>>364 episodes remaining from the two movie series he had downloaded.\n#### 364\n\nQuestion: There were 18 students assigned in a minibus for a field trip. Eight of these students were boys. On the day of the field trip, the number of girls and boys was the same since some of the girls were not able to join the trip. How many girls were not able to join the field trip?\nAnswer: 8 boys + 8 girls = <<8+8=16>>16 students joined the field trip.\nThus, 18 - 16 = <<18-16=2>>2 girls were not able to join the field trip.\n#### 2\n\nQuestion: There are 200 more red apples than green apples in a grocery store. A truck arrives and delivers another 340 green apples. If there were originally 32 green apples, how many more green apples than red apples are there in the store now?\nAnswer: There are 200 + 32 = <<200+32=232>>232 red apples.\nAfter the delivery there are 340 + 32 = <<340+32=372>>372 green apples\nThere are now 372 - 232 = <<372-232=140>>140 more green apples than red apples now.\n#### 140\n\nQuestion: A robe takes 2 bolts of blue fiber and half that much white fiber. How many bolts in total does it take?\nAnswer:" +PARAMS = SamplingParams(max_tokens=100, temperature=0) + +TOKENS = tokenizer(PROMPT, add_special_tokens=False).input_ids +TOKENS_WITH_BOS = tokenizer(PROMPT, add_special_tokens=True).input_ids + +model = LLM(MODEL) + +print("\n\n========================================") +print(tokenizer.decode(TOKENS)) +print(TOKENS[:10]) +print(TOKENS_WITH_BOS[:10]) +print("========================================\n\n") + +output = model.generate(prompt_token_ids=[TOKENS], sampling_params=PARAMS) +print(f"===== TOKENS: {output[0].outputs[0].text}") + +output = model.generate(prompt_token_ids=[TOKENS_WITH_BOS], sampling_params=PARAMS) +print(f"===== TOKENS WITHOUT WITH BOS: {output[0].outputs[0].text}") diff --git a/lm-eval.sh b/lm-eval.sh new file mode 100644 index 0000000000000..baf96bd7477d5 --- /dev/null +++ b/lm-eval.sh @@ -0,0 +1,51 @@ +#!/bin/bash +# We can use this script to compute baseline accuracy on GSM for vllm. +# We use this for fp8, which HF does not support. +# +# Make sure you have lm-eval-harness installed: +# pip install git+https://github.com/EleutherAI/lm-evaluation-harness.git@9516087b81a61d0e220b22cc1b75be76de23bc10 + +usage() { + echo`` + echo "Runs lm eval harness on GSM8k using huggingface transformers." + echo "This pathway is intended to be used to create baselines for " + echo "our automated nm-test-accuracy workflow" + echo + echo "usage: ${0} " + echo + echo " -m - huggingface stub or local directory of the model" + echo " -b - batch size to run the evaluation at" + echo " -l - limit number of samples to run" + echo " -f - number of fewshot samples to use" + echo " -t - tensor parallel size to run at" + echo +} + +while getopts "m:b:l:f:t:" OPT; do + case ${OPT} in + m ) + MODEL="$OPTARG" + ;; + b ) + BATCH_SIZE="$OPTARG" + ;; + l ) + LIMIT="$OPTARG" + ;; + f ) + FEWSHOT="$OPTARG" + ;; + t ) + TP_SIZE="$OPTARG" + ;; + \? ) + usage + exit 1 + ;; + esac +done + +lm_eval --model vllm \ + --model_args pretrained=$MODEL,tensor_parallel_size=$TP_SIZE \ + --tasks gsm8k --num_fewshot $FEWSHOT --limit $LIMIT \ + --batch_size $BATCH_SIZE --output_path $PWD/samples --write_out --log_samples \ No newline at end of file diff --git a/lm-evaluation-harness b/lm-evaluation-harness new file mode 160000 index 0000000000000..e5e5ee0cb629c --- /dev/null +++ b/lm-evaluation-harness @@ -0,0 +1 @@ +Subproject commit e5e5ee0cb629c9c88165292d1b4bf34623392d33 diff --git a/nm-vllm-env/bin/Activate.ps1 b/nm-vllm-env/bin/Activate.ps1 new file mode 100644 index 0000000000000..b49d77ba44b24 --- /dev/null +++ b/nm-vllm-env/bin/Activate.ps1 @@ -0,0 +1,247 @@ +<# +.Synopsis +Activate a Python virtual environment for the current PowerShell session. + +.Description +Pushes the python executable for a virtual environment to the front of the +$Env:PATH environment variable and sets the prompt to signify that you are +in a Python virtual environment. Makes use of the command line switches as +well as the `pyvenv.cfg` file values present in the virtual environment. + +.Parameter VenvDir +Path to the directory that contains the virtual environment to activate. The +default value for this is the parent of the directory that the Activate.ps1 +script is located within. + +.Parameter Prompt +The prompt prefix to display when this virtual environment is activated. By +default, this prompt is the name of the virtual environment folder (VenvDir) +surrounded by parentheses and followed by a single space (ie. '(.venv) '). + +.Example +Activate.ps1 +Activates the Python virtual environment that contains the Activate.ps1 script. + +.Example +Activate.ps1 -Verbose +Activates the Python virtual environment that contains the Activate.ps1 script, +and shows extra information about the activation as it executes. + +.Example +Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv +Activates the Python virtual environment located in the specified location. + +.Example +Activate.ps1 -Prompt "MyPython" +Activates the Python virtual environment that contains the Activate.ps1 script, +and prefixes the current prompt with the specified string (surrounded in +parentheses) while the virtual environment is active. + +.Notes +On Windows, it may be required to enable this Activate.ps1 script by setting the +execution policy for the user. You can do this by issuing the following PowerShell +command: + +PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser + +For more information on Execution Policies: +https://go.microsoft.com/fwlink/?LinkID=135170 + +#> +Param( + [Parameter(Mandatory = $false)] + [String] + $VenvDir, + [Parameter(Mandatory = $false)] + [String] + $Prompt +) + +<# Function declarations --------------------------------------------------- #> + +<# +.Synopsis +Remove all shell session elements added by the Activate script, including the +addition of the virtual environment's Python executable from the beginning of +the PATH variable. + +.Parameter NonDestructive +If present, do not remove this function from the global namespace for the +session. + +#> +function global:deactivate ([switch]$NonDestructive) { + # Revert to original values + + # The prior prompt: + if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { + Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt + Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT + } + + # The prior PYTHONHOME: + if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { + Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME + Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME + } + + # The prior PATH: + if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { + Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH + Remove-Item -Path Env:_OLD_VIRTUAL_PATH + } + + # Just remove the VIRTUAL_ENV altogether: + if (Test-Path -Path Env:VIRTUAL_ENV) { + Remove-Item -Path env:VIRTUAL_ENV + } + + # Just remove VIRTUAL_ENV_PROMPT altogether. + if (Test-Path -Path Env:VIRTUAL_ENV_PROMPT) { + Remove-Item -Path env:VIRTUAL_ENV_PROMPT + } + + # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: + if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { + Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force + } + + # Leave deactivate function in the global namespace if requested: + if (-not $NonDestructive) { + Remove-Item -Path function:deactivate + } +} + +<# +.Description +Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the +given folder, and returns them in a map. + +For each line in the pyvenv.cfg file, if that line can be parsed into exactly +two strings separated by `=` (with any amount of whitespace surrounding the =) +then it is considered a `key = value` line. The left hand string is the key, +the right hand is the value. + +If the value starts with a `'` or a `"` then the first and last character is +stripped from the value before being captured. + +.Parameter ConfigDir +Path to the directory that contains the `pyvenv.cfg` file. +#> +function Get-PyVenvConfig( + [String] + $ConfigDir +) { + Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg" + + # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue). + $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue + + # An empty map will be returned if no config file is found. + $pyvenvConfig = @{ } + + if ($pyvenvConfigPath) { + + Write-Verbose "File exists, parse `key = value` lines" + $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath + + $pyvenvConfigContent | ForEach-Object { + $keyval = $PSItem -split "\s*=\s*", 2 + if ($keyval[0] -and $keyval[1]) { + $val = $keyval[1] + + # Remove extraneous quotations around a string value. + if ("'""".Contains($val.Substring(0, 1))) { + $val = $val.Substring(1, $val.Length - 2) + } + + $pyvenvConfig[$keyval[0]] = $val + Write-Verbose "Adding Key: '$($keyval[0])'='$val'" + } + } + } + return $pyvenvConfig +} + + +<# Begin Activate script --------------------------------------------------- #> + +# Determine the containing directory of this script +$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition +$VenvExecDir = Get-Item -Path $VenvExecPath + +Write-Verbose "Activation script is located in path: '$VenvExecPath'" +Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)" +Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)" + +# Set values required in priority: CmdLine, ConfigFile, Default +# First, get the location of the virtual environment, it might not be +# VenvExecDir if specified on the command line. +if ($VenvDir) { + Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values" +} +else { + Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir." + $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/") + Write-Verbose "VenvDir=$VenvDir" +} + +# Next, read the `pyvenv.cfg` file to determine any required value such +# as `prompt`. +$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir + +# Next, set the prompt from the command line, or the config file, or +# just use the name of the virtual environment folder. +if ($Prompt) { + Write-Verbose "Prompt specified as argument, using '$Prompt'" +} +else { + Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value" + if ($pyvenvCfg -and $pyvenvCfg['prompt']) { + Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'" + $Prompt = $pyvenvCfg['prompt']; + } + else { + Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virtual environment)" + Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'" + $Prompt = Split-Path -Path $venvDir -Leaf + } +} + +Write-Verbose "Prompt = '$Prompt'" +Write-Verbose "VenvDir='$VenvDir'" + +# Deactivate any currently active virtual environment, but leave the +# deactivate function in place. +deactivate -nondestructive + +# Now set the environment variable VIRTUAL_ENV, used by many tools to determine +# that there is an activated venv. +$env:VIRTUAL_ENV = $VenvDir + +if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) { + + Write-Verbose "Setting prompt to '$Prompt'" + + # Set the prompt to include the env name + # Make sure _OLD_VIRTUAL_PROMPT is global + function global:_OLD_VIRTUAL_PROMPT { "" } + Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT + New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt + + function global:prompt { + Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " + _OLD_VIRTUAL_PROMPT + } + $env:VIRTUAL_ENV_PROMPT = $Prompt +} + +# Clear PYTHONHOME +if (Test-Path -Path Env:PYTHONHOME) { + Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME + Remove-Item -Path Env:PYTHONHOME +} + +# Add the venv to the PATH +Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH +$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" diff --git a/nm-vllm-env/bin/accelerate b/nm-vllm-env/bin/accelerate new file mode 100755 index 0000000000000..6b0a340979e8c --- /dev/null +++ b/nm-vllm-env/bin/accelerate @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from accelerate.commands.accelerate_cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/accelerate-config b/nm-vllm-env/bin/accelerate-config new file mode 100755 index 0000000000000..bd925ff9d67b4 --- /dev/null +++ b/nm-vllm-env/bin/accelerate-config @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from accelerate.commands.config import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/accelerate-estimate-memory b/nm-vllm-env/bin/accelerate-estimate-memory new file mode 100755 index 0000000000000..971c88fd53abf --- /dev/null +++ b/nm-vllm-env/bin/accelerate-estimate-memory @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from accelerate.commands.estimate import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/accelerate-launch b/nm-vllm-env/bin/accelerate-launch new file mode 100755 index 0000000000000..10b2004db5323 --- /dev/null +++ b/nm-vllm-env/bin/accelerate-launch @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from accelerate.commands.launch import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/accelerate-merge-weights b/nm-vllm-env/bin/accelerate-merge-weights new file mode 100755 index 0000000000000..f64c908dc8f30 --- /dev/null +++ b/nm-vllm-env/bin/accelerate-merge-weights @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from accelerate.commands.merge import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/activate b/nm-vllm-env/bin/activate new file mode 100644 index 0000000000000..a07e0c6c93c44 --- /dev/null +++ b/nm-vllm-env/bin/activate @@ -0,0 +1,69 @@ +# This file must be used with "source bin/activate" *from bash* +# you cannot run it directly + +deactivate () { + # reset old environment variables + if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then + PATH="${_OLD_VIRTUAL_PATH:-}" + export PATH + unset _OLD_VIRTUAL_PATH + fi + if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then + PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}" + export PYTHONHOME + unset _OLD_VIRTUAL_PYTHONHOME + fi + + # This should detect bash and zsh, which have a hash command that must + # be called to get it to forget past commands. Without forgetting + # past commands the $PATH changes we made may not be respected + if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r 2> /dev/null + fi + + if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then + PS1="${_OLD_VIRTUAL_PS1:-}" + export PS1 + unset _OLD_VIRTUAL_PS1 + fi + + unset VIRTUAL_ENV + unset VIRTUAL_ENV_PROMPT + if [ ! "${1:-}" = "nondestructive" ] ; then + # Self destruct! + unset -f deactivate + fi +} + +# unset irrelevant variables +deactivate nondestructive + +VIRTUAL_ENV="/home/rshaw/vllm/nm-vllm-env" +export VIRTUAL_ENV + +_OLD_VIRTUAL_PATH="$PATH" +PATH="$VIRTUAL_ENV/bin:$PATH" +export PATH + +# unset PYTHONHOME if set +# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) +# could use `if (set -u; : $PYTHONHOME) ;` in bash +if [ -n "${PYTHONHOME:-}" ] ; then + _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" + unset PYTHONHOME +fi + +if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then + _OLD_VIRTUAL_PS1="${PS1:-}" + PS1="(nm-vllm-env) ${PS1:-}" + export PS1 + VIRTUAL_ENV_PROMPT="(nm-vllm-env) " + export VIRTUAL_ENV_PROMPT +fi + +# This should detect bash and zsh, which have a hash command that must +# be called to get it to forget past commands. Without forgetting +# past commands the $PATH changes we made may not be respected +if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r 2> /dev/null +fi diff --git a/nm-vllm-env/bin/activate.csh b/nm-vllm-env/bin/activate.csh new file mode 100644 index 0000000000000..ebb9aa1ca6bbc --- /dev/null +++ b/nm-vllm-env/bin/activate.csh @@ -0,0 +1,26 @@ +# This file must be used with "source bin/activate.csh" *from csh*. +# You cannot run it directly. +# Created by Davide Di Blasi . +# Ported to Python 3.3 venv by Andrew Svetlov + +alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; unsetenv VIRTUAL_ENV_PROMPT; test "\!:*" != "nondestructive" && unalias deactivate' + +# Unset irrelevant variables. +deactivate nondestructive + +setenv VIRTUAL_ENV "/home/rshaw/vllm/nm-vllm-env" + +set _OLD_VIRTUAL_PATH="$PATH" +setenv PATH "$VIRTUAL_ENV/bin:$PATH" + + +set _OLD_VIRTUAL_PROMPT="$prompt" + +if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then + set prompt = "(nm-vllm-env) $prompt" + setenv VIRTUAL_ENV_PROMPT "(nm-vllm-env) " +endif + +alias pydoc python -m pydoc + +rehash diff --git a/nm-vllm-env/bin/activate.fish b/nm-vllm-env/bin/activate.fish new file mode 100644 index 0000000000000..616d5c92b6198 --- /dev/null +++ b/nm-vllm-env/bin/activate.fish @@ -0,0 +1,69 @@ +# This file must be used with "source /bin/activate.fish" *from fish* +# (https://fishshell.com/); you cannot run it directly. + +function deactivate -d "Exit virtual environment and return to normal shell environment" + # reset old environment variables + if test -n "$_OLD_VIRTUAL_PATH" + set -gx PATH $_OLD_VIRTUAL_PATH + set -e _OLD_VIRTUAL_PATH + end + if test -n "$_OLD_VIRTUAL_PYTHONHOME" + set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME + set -e _OLD_VIRTUAL_PYTHONHOME + end + + if test -n "$_OLD_FISH_PROMPT_OVERRIDE" + set -e _OLD_FISH_PROMPT_OVERRIDE + # prevents error when using nested fish instances (Issue #93858) + if functions -q _old_fish_prompt + functions -e fish_prompt + functions -c _old_fish_prompt fish_prompt + functions -e _old_fish_prompt + end + end + + set -e VIRTUAL_ENV + set -e VIRTUAL_ENV_PROMPT + if test "$argv[1]" != "nondestructive" + # Self-destruct! + functions -e deactivate + end +end + +# Unset irrelevant variables. +deactivate nondestructive + +set -gx VIRTUAL_ENV "/home/rshaw/vllm/nm-vllm-env" + +set -gx _OLD_VIRTUAL_PATH $PATH +set -gx PATH "$VIRTUAL_ENV/bin" $PATH + +# Unset PYTHONHOME if set. +if set -q PYTHONHOME + set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME + set -e PYTHONHOME +end + +if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" + # fish uses a function instead of an env var to generate the prompt. + + # Save the current fish_prompt function as the function _old_fish_prompt. + functions -c fish_prompt _old_fish_prompt + + # With the original prompt function renamed, we can override with our own. + function fish_prompt + # Save the return status of the last command. + set -l old_status $status + + # Output the venv prompt; color taken from the blue of the Python logo. + printf "%s%s%s" (set_color 4B8BBE) "(nm-vllm-env) " (set_color normal) + + # Restore the return status of the previous command. + echo "exit $old_status" | . + # Output the original/"old" prompt. + _old_fish_prompt + end + + set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" + set -gx VIRTUAL_ENV_PROMPT "(nm-vllm-env) " +end diff --git a/nm-vllm-env/bin/aws b/nm-vllm-env/bin/aws new file mode 100755 index 0000000000000..a82fb99a7f7e8 --- /dev/null +++ b/nm-vllm-env/bin/aws @@ -0,0 +1,27 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# Copyright 2012 Amazon.com, Inc. or its affiliates. All Rights Reserved. + +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at + +# http://aws.amazon.com/apache2.0/ + +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. +import sys +import os + +if os.environ.get('LC_CTYPE', '') == 'UTF-8': + os.environ['LC_CTYPE'] = 'en_US.UTF-8' +import awscli.clidriver + + +def main(): + return awscli.clidriver.main() + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/nm-vllm-env/bin/aws.cmd b/nm-vllm-env/bin/aws.cmd new file mode 100755 index 0000000000000..3f8caa78af448 --- /dev/null +++ b/nm-vllm-env/bin/aws.cmd @@ -0,0 +1,59 @@ +@echo OFF +REM=""" +setlocal +set PythonExe="" +set PythonExeFlags= + +for %%i in (cmd bat exe) do ( + for %%j in (python.%%i) do ( + call :SetPythonExe "%%~$PATH:j" + ) +) +for /f "tokens=2 delims==" %%i in ('assoc .py') do ( + for /f "tokens=2 delims==" %%j in ('ftype %%i') do ( + for /f "tokens=1" %%k in ("%%j") do ( + call :SetPythonExe %%k + ) + ) +) +%PythonExe% -x %PythonExeFlags% "%~f0" %* +exit /B %ERRORLEVEL% +goto :EOF + +:SetPythonExe +if not ["%~1"]==[""] ( + if [%PythonExe%]==[""] ( + set PythonExe="%~1" + ) +) +goto :EOF +""" + +# =================================================== +# Python script starts here +# =================================================== + +#!/usr/bin/env python +# Copyright 2012 Amazon.com, Inc. or its affiliates. All Rights Reserved. + +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at + +# http://aws.amazon.com/apache2.0/ + +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. + +import awscli.clidriver +import sys + + +def main(): + return awscli.clidriver.main() + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/nm-vllm-env/bin/aws_bash_completer b/nm-vllm-env/bin/aws_bash_completer new file mode 100755 index 0000000000000..32cd1dd8fd49e --- /dev/null +++ b/nm-vllm-env/bin/aws_bash_completer @@ -0,0 +1,6 @@ +# Typically that would be added under one of the following paths: +# - /etc/bash_completion.d +# - /usr/local/etc/bash_completion.d +# - /usr/share/bash-completion/completions + +complete -C aws_completer aws diff --git a/nm-vllm-env/bin/aws_completer b/nm-vllm-env/bin/aws_completer new file mode 100755 index 0000000000000..c5c638f6cefcb --- /dev/null +++ b/nm-vllm-env/bin/aws_completer @@ -0,0 +1,29 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# Copyright 2012 Amazon.com, Inc. or its affiliates. All Rights Reserved. + +# Licensed under the Apache License, Version 2.0 (the "License"). You +# may not use this file except in compliance with the License. A copy of +# the License is located at + +# http://aws.amazon.com/apache2.0/ + +# or in the "license" file accompanying this file. This file is +# distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF +# ANY KIND, either express or implied. See the License for the specific +# language governing permissions and limitations under the License. + +import os +if os.environ.get('LC_CTYPE', '') == 'UTF-8': + os.environ['LC_CTYPE'] = 'en_US.UTF-8' +import awscli.completer + +if __name__ == '__main__': + # bash exports COMP_LINE and COMP_POINT, tcsh COMMAND_LINE only + cline = os.environ.get('COMP_LINE') or os.environ.get('COMMAND_LINE') or '' + cpoint = int(os.environ.get('COMP_POINT') or len(cline)) + try: + awscli.completer.complete(cline, cpoint) + except KeyboardInterrupt: + # If the user hits Ctrl+C, we don't want to print + # a traceback to the user. + pass diff --git a/nm-vllm-env/bin/aws_zsh_completer.sh b/nm-vllm-env/bin/aws_zsh_completer.sh new file mode 100755 index 0000000000000..c1b2c124477e0 --- /dev/null +++ b/nm-vllm-env/bin/aws_zsh_completer.sh @@ -0,0 +1,60 @@ +# Source this file to activate auto completion for zsh using the bash +# compatibility helper. Make sure to run `compinit` before, which should be +# given usually. +# +# % source /path/to/zsh_complete.sh +# +# Typically that would be called somewhere in your .zshrc. +# +# Note, the overwrite of _bash_complete() is to export COMP_LINE and COMP_POINT +# That is only required for zsh <= edab1d3dbe61da7efe5f1ac0e40444b2ec9b9570 +# +# https://github.com/zsh-users/zsh/commit/edab1d3dbe61da7efe5f1ac0e40444b2ec9b9570 +# +# zsh releases prior to that version do not export the required env variables! + +autoload -Uz bashcompinit +bashcompinit -i + +_bash_complete() { + local ret=1 + local -a suf matches + local -x COMP_POINT COMP_CWORD + local -a COMP_WORDS COMPREPLY BASH_VERSINFO + local -x COMP_LINE="$words" + local -A savejobstates savejobtexts + + (( COMP_POINT = 1 + ${#${(j. .)words[1,CURRENT]}} + $#QIPREFIX + $#IPREFIX + $#PREFIX )) + (( COMP_CWORD = CURRENT - 1)) + COMP_WORDS=( $words ) + BASH_VERSINFO=( 2 05b 0 1 release ) + + savejobstates=( ${(kv)jobstates} ) + savejobtexts=( ${(kv)jobtexts} ) + + [[ ${argv[${argv[(I)nospace]:-0}-1]} = -o ]] && suf=( -S '' ) + + matches=( ${(f)"$(compgen $@ -- ${words[CURRENT]})"} ) + + if [[ -n $matches ]]; then + if [[ ${argv[${argv[(I)filenames]:-0}-1]} = -o ]]; then + compset -P '*/' && matches=( ${matches##*/} ) + compset -S '/*' && matches=( ${matches%%/*} ) + compadd -Q -f "${suf[@]}" -a matches && ret=0 + else + compadd -Q "${suf[@]}" -a matches && ret=0 + fi + fi + + if (( ret )); then + if [[ ${argv[${argv[(I)default]:-0}-1]} = -o ]]; then + _default "${suf[@]}" && ret=0 + elif [[ ${argv[${argv[(I)dirnames]:-0}-1]} = -o ]]; then + _directories "${suf[@]}" && ret=0 + fi + fi + + return ret +} + +complete -C aws_completer aws diff --git a/nm-vllm-env/bin/backend-test-tools b/nm-vllm-env/bin/backend-test-tools new file mode 100755 index 0000000000000..af40849553a65 --- /dev/null +++ b/nm-vllm-env/bin/backend-test-tools @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from onnx.backend.test.cmd_tools import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/chardetect b/nm-vllm-env/bin/chardetect new file mode 100755 index 0000000000000..5a2761cd83a0b --- /dev/null +++ b/nm-vllm-env/bin/chardetect @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from chardet.cli.chardetect import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/check-model b/nm-vllm-env/bin/check-model new file mode 100755 index 0000000000000..9fe24cfbd3f65 --- /dev/null +++ b/nm-vllm-env/bin/check-model @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from onnx.bin.checker import check_model +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(check_model()) diff --git a/nm-vllm-env/bin/check-node b/nm-vllm-env/bin/check-node new file mode 100755 index 0000000000000..9773cf5e0651e --- /dev/null +++ b/nm-vllm-env/bin/check-node @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from onnx.bin.checker import check_node +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(check_node()) diff --git a/nm-vllm-env/bin/clang-format b/nm-vllm-env/bin/clang-format new file mode 100755 index 0000000000000..eb245fc09483e --- /dev/null +++ b/nm-vllm-env/bin/clang-format @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from clang_format import clang_format +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(clang_format()) diff --git a/nm-vllm-env/bin/clang-format-diff.py b/nm-vllm-env/bin/clang-format-diff.py new file mode 100755 index 0000000000000..00070be00ba17 --- /dev/null +++ b/nm-vllm-env/bin/clang-format-diff.py @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from clang_format.clang_format_diff import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/cmake b/nm-vllm-env/bin/cmake new file mode 100755 index 0000000000000..ef5f7841f032f --- /dev/null +++ b/nm-vllm-env/bin/cmake @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from cmake import cmake +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cmake()) diff --git a/nm-vllm-env/bin/codespell b/nm-vllm-env/bin/codespell new file mode 100755 index 0000000000000..19400af40e989 --- /dev/null +++ b/nm-vllm-env/bin/codespell @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from codespell_lib import _script_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(_script_main()) diff --git a/nm-vllm-env/bin/coloredlogs b/nm-vllm-env/bin/coloredlogs new file mode 100755 index 0000000000000..059dba0fb6c92 --- /dev/null +++ b/nm-vllm-env/bin/coloredlogs @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from coloredlogs.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/convert-caffe2-to-onnx b/nm-vllm-env/bin/convert-caffe2-to-onnx new file mode 100755 index 0000000000000..0e345764dc68c --- /dev/null +++ b/nm-vllm-env/bin/convert-caffe2-to-onnx @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from caffe2.python.onnx.bin.conversion import caffe2_to_onnx +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(caffe2_to_onnx()) diff --git a/nm-vllm-env/bin/convert-onnx-to-caffe2 b/nm-vllm-env/bin/convert-onnx-to-caffe2 new file mode 100755 index 0000000000000..a3d1e7e9de1ed --- /dev/null +++ b/nm-vllm-env/bin/convert-onnx-to-caffe2 @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from caffe2.python.onnx.bin.conversion import onnx_to_caffe2 +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(onnx_to_caffe2()) diff --git a/nm-vllm-env/bin/cpack b/nm-vllm-env/bin/cpack new file mode 100755 index 0000000000000..a3e31fe63b549 --- /dev/null +++ b/nm-vllm-env/bin/cpack @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from cmake import cpack +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cpack()) diff --git a/nm-vllm-env/bin/cpuinfo b/nm-vllm-env/bin/cpuinfo new file mode 100755 index 0000000000000..f87893de5336e --- /dev/null +++ b/nm-vllm-env/bin/cpuinfo @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from cpuinfo import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/ctest b/nm-vllm-env/bin/ctest new file mode 100755 index 0000000000000..faaa8c697dd2f --- /dev/null +++ b/nm-vllm-env/bin/ctest @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from cmake import ctest +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(ctest()) diff --git a/nm-vllm-env/bin/datasets-cli b/nm-vllm-env/bin/datasets-cli new file mode 100755 index 0000000000000..4c29779958548 --- /dev/null +++ b/nm-vllm-env/bin/datasets-cli @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from datasets.commands.datasets_cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/distro b/nm-vllm-env/bin/distro new file mode 100755 index 0000000000000..46a7f5a750b14 --- /dev/null +++ b/nm-vllm-env/bin/distro @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from distro.distro import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/dmypy b/nm-vllm-env/bin/dmypy new file mode 100755 index 0000000000000..3e559397a22ba --- /dev/null +++ b/nm-vllm-env/bin/dmypy @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from mypy.dmypy.client import console_entry +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(console_entry()) diff --git a/nm-vllm-env/bin/dotenv b/nm-vllm-env/bin/dotenv new file mode 100755 index 0000000000000..e662e27db76ef --- /dev/null +++ b/nm-vllm-env/bin/dotenv @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from dotenv.__main__ import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/nm-vllm-env/bin/email_validator b/nm-vllm-env/bin/email_validator new file mode 100755 index 0000000000000..86b6c23e063fd --- /dev/null +++ b/nm-vllm-env/bin/email_validator @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from email_validator.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/evaluate-cli b/nm-vllm-env/bin/evaluate-cli new file mode 100755 index 0000000000000..39885a315d30e --- /dev/null +++ b/nm-vllm-env/bin/evaluate-cli @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from evaluate.commands.evaluate_cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/f2py b/nm-vllm-env/bin/f2py new file mode 100755 index 0000000000000..1d158aee2f869 --- /dev/null +++ b/nm-vllm-env/bin/f2py @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from numpy.f2py.f2py2e import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/fastapi b/nm-vllm-env/bin/fastapi new file mode 100755 index 0000000000000..c358ec20507c3 --- /dev/null +++ b/nm-vllm-env/bin/fastapi @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from fastapi_cli.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/fonttools b/nm-vllm-env/bin/fonttools new file mode 100755 index 0000000000000..b1f9cc6c44782 --- /dev/null +++ b/nm-vllm-env/bin/fonttools @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/futurize b/nm-vllm-env/bin/futurize new file mode 100755 index 0000000000000..f84c05a0e0b87 --- /dev/null +++ b/nm-vllm-env/bin/futurize @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from libfuturize.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/geocode b/nm-vllm-env/bin/geocode new file mode 100755 index 0000000000000..76a8eca214af1 --- /dev/null +++ b/nm-vllm-env/bin/geocode @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from geocoder.cli import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/nm-vllm-env/bin/get_gprof b/nm-vllm-env/bin/get_gprof new file mode 100755 index 0000000000000..b759dcbfccab2 --- /dev/null +++ b/nm-vllm-env/bin/get_gprof @@ -0,0 +1,75 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# +# Author: Mike McKerns (mmckerns @caltech and @uqfoundation) +# Copyright (c) 2008-2016 California Institute of Technology. +# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation. +# License: 3-clause BSD. The full license text is available at: +# - https://github.com/uqfoundation/dill/blob/master/LICENSE +''' +build profile graph for the given instance + +running: + $ get_gprof + +executes: + gprof2dot -f pstats .prof | dot -Tpng -o .call.png + +where: + are arguments for gprof2dot, such as "-n 5 -e 5" + is code to create the instance to profile + is the class of the instance (i.e. type(instance)) + +For example: + $ get_gprof -n 5 -e 1 "import numpy; numpy.array([1,2])" + +will create 'ndarray.call.png' with the profile graph for numpy.array([1,2]), +where '-n 5' eliminates nodes below 5% threshold, similarly '-e 1' eliminates +edges below 1% threshold +''' + +if __name__ == "__main__": + import sys + if len(sys.argv) < 2: + print ("Please provide an object instance (e.g. 'import math; math.pi')") + sys.exit() + # grab args for gprof2dot + args = sys.argv[1:-1] + args = ' '.join(args) + # last arg builds the object + obj = sys.argv[-1] + obj = obj.split(';') + # multi-line prep for generating an instance + for line in obj[:-1]: + exec(line) + # one-line generation of an instance + try: + obj = eval(obj[-1]) + except Exception: + print ("Error processing object instance") + sys.exit() + + # get object 'name' + objtype = type(obj) + name = getattr(objtype, '__name__', getattr(objtype, '__class__', objtype)) + + # profile dumping an object + import dill + import os + import cProfile + #name = os.path.splitext(os.path.basename(__file__))[0] + cProfile.run("dill.dumps(obj)", filename="%s.prof" % name) + msg = "gprof2dot -f pstats %s %s.prof | dot -Tpng -o %s.call.png" % (args, name, name) + try: + res = os.system(msg) + except Exception: + print ("Please verify install of 'gprof2dot' to view profile graphs") + if res: + print ("Please verify install of 'gprof2dot' to view profile graphs") + + # get stats + f_prof = "%s.prof" % name + import pstats + stats = pstats.Stats(f_prof, stream=sys.stdout) + stats.strip_dirs().sort_stats('cumtime') + stats.print_stats(20) #XXX: save to file instead of print top 20? + os.remove(f_prof) diff --git a/nm-vllm-env/bin/get_objgraph b/nm-vllm-env/bin/get_objgraph new file mode 100755 index 0000000000000..32da8c99c17de --- /dev/null +++ b/nm-vllm-env/bin/get_objgraph @@ -0,0 +1,54 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# +# Author: Mike McKerns (mmckerns @caltech and @uqfoundation) +# Copyright (c) 2008-2016 California Institute of Technology. +# Copyright (c) 2016-2024 The Uncertainty Quantification Foundation. +# License: 3-clause BSD. The full license text is available at: +# - https://github.com/uqfoundation/dill/blob/master/LICENSE +""" +display the reference paths for objects in ``dill.types`` or a .pkl file + +Notes: + the generated image is useful in showing the pointer references in + objects that are or can be pickled. Any object in ``dill.objects`` + listed in ``dill.load_types(picklable=True, unpicklable=True)`` works. + +Examples:: + + $ get_objgraph ArrayType + Image generated as ArrayType.png +""" + +import dill as pickle +#pickle.debug.trace(True) +#import pickle + +# get all objects for testing +from dill import load_types +load_types(pickleable=True,unpickleable=True) +from dill import objects + +if __name__ == "__main__": + import sys + if len(sys.argv) != 2: + print ("Please provide exactly one file or type name (e.g. 'IntType')") + msg = "\n" + for objtype in list(objects.keys())[:40]: + msg += objtype + ', ' + print (msg + "...") + else: + objtype = str(sys.argv[-1]) + try: + obj = objects[objtype] + except KeyError: + obj = pickle.load(open(objtype,'rb')) + import os + objtype = os.path.splitext(objtype)[0] + try: + import objgraph + objgraph.show_refs(obj, filename=objtype+'.png') + except ImportError: + print ("Please install 'objgraph' to view object graphs") + + +# EOF diff --git a/nm-vllm-env/bin/git-clang-format b/nm-vllm-env/bin/git-clang-format new file mode 100755 index 0000000000000..9acff42f2282b --- /dev/null +++ b/nm-vllm-env/bin/git-clang-format @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from clang_format.git_clang_format import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/httpx b/nm-vllm-env/bin/httpx new file mode 100755 index 0000000000000..f95d054555318 --- /dev/null +++ b/nm-vllm-env/bin/httpx @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from httpx import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/huggingface-cli b/nm-vllm-env/bin/huggingface-cli new file mode 100755 index 0000000000000..11b628f11373c --- /dev/null +++ b/nm-vllm-env/bin/huggingface-cli @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from huggingface_hub.commands.huggingface_cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/humanfriendly b/nm-vllm-env/bin/humanfriendly new file mode 100755 index 0000000000000..d1e0d9f4abdf5 --- /dev/null +++ b/nm-vllm-env/bin/humanfriendly @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from humanfriendly.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/isort b/nm-vllm-env/bin/isort new file mode 100755 index 0000000000000..d8cc4d3f20dba --- /dev/null +++ b/nm-vllm-env/bin/isort @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from isort.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/isort-identify-imports b/nm-vllm-env/bin/isort-identify-imports new file mode 100755 index 0000000000000..2e73da7438d63 --- /dev/null +++ b/nm-vllm-env/bin/isort-identify-imports @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from isort.main import identify_imports_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(identify_imports_main()) diff --git a/nm-vllm-env/bin/isympy b/nm-vllm-env/bin/isympy new file mode 100755 index 0000000000000..5c78b4a7a74b5 --- /dev/null +++ b/nm-vllm-env/bin/isympy @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from isympy import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/jp.py b/nm-vllm-env/bin/jp.py new file mode 100755 index 0000000000000..cda02ed1b7f41 --- /dev/null +++ b/nm-vllm-env/bin/jp.py @@ -0,0 +1,54 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +import sys +import json +import argparse +from pprint import pformat + +import jmespath +from jmespath import exceptions + + +def main(): + parser = argparse.ArgumentParser() + parser.add_argument('expression') + parser.add_argument('-f', '--filename', + help=('The filename containing the input data. ' + 'If a filename is not given then data is ' + 'read from stdin.')) + parser.add_argument('--ast', action='store_true', + help=('Pretty print the AST, do not search the data.')) + args = parser.parse_args() + expression = args.expression + if args.ast: + # Only print the AST + expression = jmespath.compile(args.expression) + sys.stdout.write(pformat(expression.parsed)) + sys.stdout.write('\n') + return 0 + if args.filename: + with open(args.filename, 'r') as f: + data = json.load(f) + else: + data = sys.stdin.read() + data = json.loads(data) + try: + sys.stdout.write(json.dumps( + jmespath.search(expression, data), indent=4, ensure_ascii=False)) + sys.stdout.write('\n') + except exceptions.ArityError as e: + sys.stderr.write("invalid-arity: %s\n" % e) + return 1 + except exceptions.JMESPathTypeError as e: + sys.stderr.write("invalid-type: %s\n" % e) + return 1 + except exceptions.UnknownFunctionError as e: + sys.stderr.write("unknown-function: %s\n" % e) + return 1 + except exceptions.ParseError as e: + sys.stderr.write("syntax-error: %s\n" % e) + return 1 + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/nm-vllm-env/bin/jsonschema b/nm-vllm-env/bin/jsonschema new file mode 100755 index 0000000000000..92d6e7296fbba --- /dev/null +++ b/nm-vllm-env/bin/jsonschema @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from jsonschema.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/lm-eval b/nm-vllm-env/bin/lm-eval new file mode 100755 index 0000000000000..5868d58be5fa5 --- /dev/null +++ b/nm-vllm-env/bin/lm-eval @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from lm_eval.__main__ import cli_evaluate +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli_evaluate()) diff --git a/nm-vllm-env/bin/lm_eval b/nm-vllm-env/bin/lm_eval new file mode 100755 index 0000000000000..5868d58be5fa5 --- /dev/null +++ b/nm-vllm-env/bin/lm_eval @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from lm_eval.__main__ import cli_evaluate +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli_evaluate()) diff --git a/nm-vllm-env/bin/markdown-it b/nm-vllm-env/bin/markdown-it new file mode 100755 index 0000000000000..6990794fa8c1d --- /dev/null +++ b/nm-vllm-env/bin/markdown-it @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from markdown_it.cli.parse import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/mypy b/nm-vllm-env/bin/mypy new file mode 100755 index 0000000000000..c20cb857c0c4a --- /dev/null +++ b/nm-vllm-env/bin/mypy @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from mypy.__main__ import console_entry +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(console_entry()) diff --git a/nm-vllm-env/bin/mypyc b/nm-vllm-env/bin/mypyc new file mode 100755 index 0000000000000..f8b54c9da9ccf --- /dev/null +++ b/nm-vllm-env/bin/mypyc @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from mypyc.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/ninja b/nm-vllm-env/bin/ninja new file mode 100755 index 0000000000000..8e1b7f018539e --- /dev/null +++ b/nm-vllm-env/bin/ninja @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from ninja import ninja +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(ninja()) diff --git a/nm-vllm-env/bin/nltk b/nm-vllm-env/bin/nltk new file mode 100755 index 0000000000000..d6cd4d18b14d7 --- /dev/null +++ b/nm-vllm-env/bin/nltk @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from nltk.cli import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/nm-vllm-env/bin/normalizer b/nm-vllm-env/bin/normalizer new file mode 100755 index 0000000000000..d5395e4d262f4 --- /dev/null +++ b/nm-vllm-env/bin/normalizer @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from charset_normalizer.cli import cli_detect +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli_detect()) diff --git a/nm-vllm-env/bin/numba b/nm-vllm-env/bin/numba new file mode 100755 index 0000000000000..674358b12010a --- /dev/null +++ b/nm-vllm-env/bin/numba @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: UTF-8 -*- +from __future__ import print_function, division, absolute_import + +from numba.misc.numba_entry import main + +if __name__ == "__main__": + main() diff --git a/nm-vllm-env/bin/onnxruntime_test b/nm-vllm-env/bin/onnxruntime_test new file mode 100755 index 0000000000000..79cf47f88ec62 --- /dev/null +++ b/nm-vllm-env/bin/onnxruntime_test @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from onnxruntime.tools.onnxruntime_test import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/openai b/nm-vllm-env/bin/openai new file mode 100755 index 0000000000000..ae339818bfff5 --- /dev/null +++ b/nm-vllm-env/bin/openai @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from openai.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pasteurize b/nm-vllm-env/bin/pasteurize new file mode 100755 index 0000000000000..768b3e89d9855 --- /dev/null +++ b/nm-vllm-env/bin/pasteurize @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from libpasteurize.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pip b/nm-vllm-env/bin/pip new file mode 100755 index 0000000000000..0b50ffc6e134c --- /dev/null +++ b/nm-vllm-env/bin/pip @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pip3 b/nm-vllm-env/bin/pip3 new file mode 100755 index 0000000000000..0b50ffc6e134c --- /dev/null +++ b/nm-vllm-env/bin/pip3 @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pip3.10 b/nm-vllm-env/bin/pip3.10 new file mode 100755 index 0000000000000..0b50ffc6e134c --- /dev/null +++ b/nm-vllm-env/bin/pip3.10 @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/py.test b/nm-vllm-env/bin/py.test new file mode 100755 index 0000000000000..ecae7b8fef3dd --- /dev/null +++ b/nm-vllm-env/bin/py.test @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pytest import console_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(console_main()) diff --git a/nm-vllm-env/bin/pyairports b/nm-vllm-env/bin/pyairports new file mode 100755 index 0000000000000..1473fa81c648e --- /dev/null +++ b/nm-vllm-env/bin/pyairports @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pyairports.airports import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pybind11-config b/nm-vllm-env/bin/pybind11-config new file mode 100755 index 0000000000000..56a655985f458 --- /dev/null +++ b/nm-vllm-env/bin/pybind11-config @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pybind11.__main__ import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pyftmerge b/nm-vllm-env/bin/pyftmerge new file mode 100755 index 0000000000000..b4ce33dc196a4 --- /dev/null +++ b/nm-vllm-env/bin/pyftmerge @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.merge import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pyftsubset b/nm-vllm-env/bin/pyftsubset new file mode 100755 index 0000000000000..34e804f17521f --- /dev/null +++ b/nm-vllm-env/bin/pyftsubset @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from fontTools.subset import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pygmentize b/nm-vllm-env/bin/pygmentize new file mode 100755 index 0000000000000..7b9f18d96e2a9 --- /dev/null +++ b/nm-vllm-env/bin/pygmentize @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pygments.cmdline import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/pyrsa-decrypt b/nm-vllm-env/bin/pyrsa-decrypt new file mode 100755 index 0000000000000..50665c0e8c85e --- /dev/null +++ b/nm-vllm-env/bin/pyrsa-decrypt @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from rsa.cli import decrypt +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(decrypt()) diff --git a/nm-vllm-env/bin/pyrsa-encrypt b/nm-vllm-env/bin/pyrsa-encrypt new file mode 100755 index 0000000000000..6a8039df507fc --- /dev/null +++ b/nm-vllm-env/bin/pyrsa-encrypt @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from rsa.cli import encrypt +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(encrypt()) diff --git a/nm-vllm-env/bin/pyrsa-keygen b/nm-vllm-env/bin/pyrsa-keygen new file mode 100755 index 0000000000000..1943bd4f33af3 --- /dev/null +++ b/nm-vllm-env/bin/pyrsa-keygen @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from rsa.cli import keygen +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(keygen()) diff --git a/nm-vllm-env/bin/pyrsa-priv2pub b/nm-vllm-env/bin/pyrsa-priv2pub new file mode 100755 index 0000000000000..0f14f8bccba5e --- /dev/null +++ b/nm-vllm-env/bin/pyrsa-priv2pub @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from rsa.util import private_to_public +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(private_to_public()) diff --git a/nm-vllm-env/bin/pyrsa-sign b/nm-vllm-env/bin/pyrsa-sign new file mode 100755 index 0000000000000..e6f51012cfa98 --- /dev/null +++ b/nm-vllm-env/bin/pyrsa-sign @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from rsa.cli import sign +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(sign()) diff --git a/nm-vllm-env/bin/pyrsa-verify b/nm-vllm-env/bin/pyrsa-verify new file mode 100755 index 0000000000000..674c37366c91a --- /dev/null +++ b/nm-vllm-env/bin/pyrsa-verify @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from rsa.cli import verify +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(verify()) diff --git a/nm-vllm-env/bin/pytest b/nm-vllm-env/bin/pytest new file mode 100755 index 0000000000000..ecae7b8fef3dd --- /dev/null +++ b/nm-vllm-env/bin/pytest @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from pytest import console_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(console_main()) diff --git a/nm-vllm-env/bin/python b/nm-vllm-env/bin/python new file mode 120000 index 0000000000000..b8a0adbbb97ea --- /dev/null +++ b/nm-vllm-env/bin/python @@ -0,0 +1 @@ +python3 \ No newline at end of file diff --git a/nm-vllm-env/bin/python3 b/nm-vllm-env/bin/python3 new file mode 120000 index 0000000000000..738f6e9c880e3 --- /dev/null +++ b/nm-vllm-env/bin/python3 @@ -0,0 +1 @@ +/home/rshaw/.pyenv/versions/3.10.14/bin/python3 \ No newline at end of file diff --git a/nm-vllm-env/bin/python3.10 b/nm-vllm-env/bin/python3.10 new file mode 120000 index 0000000000000..b8a0adbbb97ea --- /dev/null +++ b/nm-vllm-env/bin/python3.10 @@ -0,0 +1 @@ +python3 \ No newline at end of file diff --git a/nm-vllm-env/bin/ray b/nm-vllm-env/bin/ray new file mode 100755 index 0000000000000..5e6cd27a94ef7 --- /dev/null +++ b/nm-vllm-env/bin/ray @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from ray.scripts.scripts import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/nm-vllm-env/bin/rllib b/nm-vllm-env/bin/rllib new file mode 100755 index 0000000000000..6a42f8be7d586 --- /dev/null +++ b/nm-vllm-env/bin/rllib @@ -0,0 +1,8 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf-8 -*- +import re +import sys +from ray.rllib.scripts import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/nm-vllm-env/bin/rst2html.py b/nm-vllm-env/bin/rst2html.py new file mode 100755 index 0000000000000..6e1bfe437270d --- /dev/null +++ b/nm-vllm-env/bin/rst2html.py @@ -0,0 +1,23 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2html.py 4564 2006-05-21 20:44:42Z wiemann $ +# Author: David Goodger +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing HTML. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description + + +description = ('Generates (X)HTML documents from standalone reStructuredText ' + 'sources. ' + default_description) + +publish_cmdline(writer_name='html', description=description) diff --git a/nm-vllm-env/bin/rst2html4.py b/nm-vllm-env/bin/rst2html4.py new file mode 100755 index 0000000000000..9197b0bdae06f --- /dev/null +++ b/nm-vllm-env/bin/rst2html4.py @@ -0,0 +1,26 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2html4.py 7994 2016-12-10 17:41:45Z milde $ +# Author: David Goodger +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing (X)HTML. + +The output conforms to XHTML 1.0 transitional +and almost to HTML 4.01 transitional (except for closing empty tags). +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description + + +description = ('Generates (X)HTML documents from standalone reStructuredText ' + 'sources. ' + default_description) + +publish_cmdline(writer_name='html4', description=description) diff --git a/nm-vllm-env/bin/rst2html5.py b/nm-vllm-env/bin/rst2html5.py new file mode 100755 index 0000000000000..609546f3a4468 --- /dev/null +++ b/nm-vllm-env/bin/rst2html5.py @@ -0,0 +1,35 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 +# -*- coding: utf8 -*- +# :Copyright: © 2015 Günter Milde. +# :License: Released under the terms of the `2-Clause BSD license`_, in short: +# +# Copying and distribution of this file, with or without modification, +# are permitted in any medium without royalty provided the copyright +# notice and this notice are preserved. +# This file is offered as-is, without any warranty. +# +# .. _2-Clause BSD license: http://www.spdx.org/licenses/BSD-2-Clause +# +# Revision: $Revision: 8410 $ +# Date: $Date: 2019-11-04 22:14:43 +0100 (Mo, 04. Nov 2019) $ + +""" +A minimal front end to the Docutils Publisher, producing HTML 5 documents. + +The output also conforms to XHTML 1.0 transitional +(except for the doctype declaration). +""" + +try: + import locale # module missing in Jython + locale.setlocale(locale.LC_ALL, '') +except locale.Error: + pass + +from docutils.core import publish_cmdline, default_description + +description = (u'Generates HTML 5 documents from standalone ' + u'reStructuredText sources ' + + default_description) + +publish_cmdline(writer_name='html5', description=description) diff --git a/nm-vllm-env/bin/rst2latex.py b/nm-vllm-env/bin/rst2latex.py new file mode 100755 index 0000000000000..74a8d1e432921 --- /dev/null +++ b/nm-vllm-env/bin/rst2latex.py @@ -0,0 +1,26 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2latex.py 5905 2009-04-16 12:04:49Z milde $ +# Author: David Goodger +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing LaTeX. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline + +description = ('Generates LaTeX documents from standalone reStructuredText ' + 'sources. ' + 'Reads from (default is stdin) and writes to ' + ' (default is stdout). See ' + ' for ' + 'the full reference.') + +publish_cmdline(writer_name='latex', description=description) diff --git a/nm-vllm-env/bin/rst2man.py b/nm-vllm-env/bin/rst2man.py new file mode 100755 index 0000000000000..6d4f400db55f3 --- /dev/null +++ b/nm-vllm-env/bin/rst2man.py @@ -0,0 +1,26 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# Author: +# Contact: grubert@users.sf.net +# Copyright: This module has been placed in the public domain. + +""" +man.py +====== + +This module provides a simple command line interface that uses the +man page writer to output from ReStructuredText source. +""" + +import locale +try: + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description +from docutils.writers import manpage + +description = ("Generates plain unix manual documents. " + default_description) + +publish_cmdline(writer=manpage.Writer(), description=description) diff --git a/nm-vllm-env/bin/rst2odt.py b/nm-vllm-env/bin/rst2odt.py new file mode 100755 index 0000000000000..b9561be25a5c1 --- /dev/null +++ b/nm-vllm-env/bin/rst2odt.py @@ -0,0 +1,30 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2odt.py 5839 2009-01-07 19:09:28Z dkuhlman $ +# Author: Dave Kuhlman +# Copyright: This module has been placed in the public domain. + +""" +A front end to the Docutils Publisher, producing OpenOffice documents. +""" + +import sys +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline_to_binary, default_description +from docutils.writers.odf_odt import Writer, Reader + + +description = ('Generates OpenDocument/OpenOffice/ODF documents from ' + 'standalone reStructuredText sources. ' + default_description) + + +writer = Writer() +reader = Reader() +output = publish_cmdline_to_binary(reader=reader, writer=writer, + description=description) + diff --git a/nm-vllm-env/bin/rst2odt_prepstyles.py b/nm-vllm-env/bin/rst2odt_prepstyles.py new file mode 100755 index 0000000000000..0074d0f8a614e --- /dev/null +++ b/nm-vllm-env/bin/rst2odt_prepstyles.py @@ -0,0 +1,67 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2odt_prepstyles.py 8346 2019-08-26 12:11:32Z milde $ +# Author: Dave Kuhlman +# Copyright: This module has been placed in the public domain. + +""" +Fix a word-processor-generated styles.odt for odtwriter use: Drop page size +specifications from styles.xml in STYLE_FILE.odt. +""" + +# Author: Michael Schutte + +from __future__ import print_function + +from lxml import etree +import sys +import zipfile +from tempfile import mkstemp +import shutil +import os + +NAMESPACES = { + "style": "urn:oasis:names:tc:opendocument:xmlns:style:1.0", + "fo": "urn:oasis:names:tc:opendocument:xmlns:xsl-fo-compatible:1.0" +} + + +def prepstyle(filename): + + zin = zipfile.ZipFile(filename) + styles = zin.read("styles.xml") + + root = etree.fromstring(styles) + for el in root.xpath("//style:page-layout-properties", + namespaces=NAMESPACES): + for attr in el.attrib: + if attr.startswith("{%s}" % NAMESPACES["fo"]): + del el.attrib[attr] + + tempname = mkstemp() + zout = zipfile.ZipFile(os.fdopen(tempname[0], "w"), "w", + zipfile.ZIP_DEFLATED) + + for item in zin.infolist(): + if item.filename == "styles.xml": + zout.writestr(item, etree.tostring(root)) + else: + zout.writestr(item, zin.read(item.filename)) + + zout.close() + zin.close() + shutil.move(tempname[1], filename) + + +def main(): + args = sys.argv[1:] + if len(args) != 1: + print(__doc__, file=sys.stderr) + print("Usage: %s STYLE_FILE.odt\n" % sys.argv[0], file=sys.stderr) + sys.exit(1) + filename = args[0] + prepstyle(filename) + + +if __name__ == '__main__': + main() diff --git a/nm-vllm-env/bin/rst2pseudoxml.py b/nm-vllm-env/bin/rst2pseudoxml.py new file mode 100755 index 0000000000000..b09d0def5fcba --- /dev/null +++ b/nm-vllm-env/bin/rst2pseudoxml.py @@ -0,0 +1,23 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2pseudoxml.py 4564 2006-05-21 20:44:42Z wiemann $ +# Author: David Goodger +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing pseudo-XML. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description + + +description = ('Generates pseudo-XML from standalone reStructuredText ' + 'sources (for testing purposes). ' + default_description) + +publish_cmdline(description=description) diff --git a/nm-vllm-env/bin/rst2s5.py b/nm-vllm-env/bin/rst2s5.py new file mode 100755 index 0000000000000..ba70d4147faab --- /dev/null +++ b/nm-vllm-env/bin/rst2s5.py @@ -0,0 +1,24 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2s5.py 4564 2006-05-21 20:44:42Z wiemann $ +# Author: Chris Liechti +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing HTML slides using +the S5 template system. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description + + +description = ('Generates S5 (X)HTML slideshow documents from standalone ' + 'reStructuredText sources. ' + default_description) + +publish_cmdline(writer_name='s5', description=description) diff --git a/nm-vllm-env/bin/rst2xetex.py b/nm-vllm-env/bin/rst2xetex.py new file mode 100755 index 0000000000000..db47ed92f1dad --- /dev/null +++ b/nm-vllm-env/bin/rst2xetex.py @@ -0,0 +1,27 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2xetex.py 7847 2015-03-17 17:30:47Z milde $ +# Author: Guenter Milde +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing Lua/XeLaTeX code. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline + +description = ('Generates LaTeX documents from standalone reStructuredText ' + 'sources for compilation with the Unicode-aware TeX variants ' + 'XeLaTeX or LuaLaTeX. ' + 'Reads from (default is stdin) and writes to ' + ' (default is stdout). See ' + ' for ' + 'the full reference.') + +publish_cmdline(writer_name='xetex', description=description) diff --git a/nm-vllm-env/bin/rst2xml.py b/nm-vllm-env/bin/rst2xml.py new file mode 100755 index 0000000000000..282e06582a049 --- /dev/null +++ b/nm-vllm-env/bin/rst2xml.py @@ -0,0 +1,23 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rst2xml.py 4564 2006-05-21 20:44:42Z wiemann $ +# Author: David Goodger +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing Docutils XML. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description + + +description = ('Generates Docutils-native XML from standalone ' + 'reStructuredText sources. ' + default_description) + +publish_cmdline(writer_name='xml', description=description) diff --git a/nm-vllm-env/bin/rstpep2html.py b/nm-vllm-env/bin/rstpep2html.py new file mode 100755 index 0000000000000..cf0a49f83df38 --- /dev/null +++ b/nm-vllm-env/bin/rstpep2html.py @@ -0,0 +1,25 @@ +#!/home/rshaw/vllm/nm-vllm-env/bin/python3 + +# $Id: rstpep2html.py 4564 2006-05-21 20:44:42Z wiemann $ +# Author: David Goodger +# Copyright: This module has been placed in the public domain. + +""" +A minimal front end to the Docutils Publisher, producing HTML from PEP +(Python Enhancement Proposal) documents. +""" + +try: + import locale + locale.setlocale(locale.LC_ALL, '') +except: + pass + +from docutils.core import publish_cmdline, default_description + + +description = ('Generates (X)HTML from reStructuredText-format PEP files. 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