diff --git a/.gitattributes b/.gitattributes index 9855cf6b2ca6165be2e09bb4e8c25661908ed9d4..8202f0e21de715c0417aa56d0b3cb36f31ed6e72 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1622,3 +1622,9 @@ vllm/lib/python3.10/site-packages/pycountry/locales/uk/LC_MESSAGES/iso639-3.mo f vllm/lib/python3.10/site-packages/pycountry/locales/fr/LC_MESSAGES/iso639-3.mo filter=lfs diff=lfs merge=lfs -text parrot/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text parrot/lib/python3.10/site-packages/scipy/_lib/_ccallback_c.cpython-310-x86_64-linux-gnu.so filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/pycountry/locales/be/LC_MESSAGES/iso3166-2.mo filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/pycountry/locales/fr/LC_MESSAGES/iso3166-2.mo filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/pycountry/locales/uk/LC_MESSAGES/iso3166-2.mo filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/pycountry/locales/tr/LC_MESSAGES/iso639-3.mo filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/pycountry/locales/ast/LC_MESSAGES/iso639-3.mo filter=lfs diff=lfs merge=lfs -text +vllm/lib/python3.10/site-packages/pycountry/locales/sr/LC_MESSAGES/iso3166-2.mo filter=lfs diff=lfs merge=lfs -text diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/gammainc_asy.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/gammainc_asy.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a04ae9f52d6ed2e952b214bbf88a6b58c634c74c Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/gammainc_asy.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/hyp2f1_data.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/hyp2f1_data.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7e0d86c98c767b7e0641a61d969ed62710b4c40f Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/hyp2f1_data.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/loggamma.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/loggamma.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5faf8e51886fc10cbfdcc3a523fe97011446a56c Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/loggamma.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/wrightomega.cpython-310.pyc b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/wrightomega.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7e9112bcc50c0fbdc2a6d16504c51c772f5ddfe0 Binary files /dev/null and b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/__pycache__/wrightomega.cpython-310.pyc differ diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/lambertw.py b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/lambertw.py new file mode 100644 index 0000000000000000000000000000000000000000..1fdbf35b2cf85f1f7a6e73579546ed5cfe508fa6 --- /dev/null +++ b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/lambertw.py @@ -0,0 +1,68 @@ +"""Compute a Pade approximation for the principal branch of the +Lambert W function around 0 and compare it to various other +approximations. + +""" +import numpy as np + +try: + import mpmath + import matplotlib.pyplot as plt +except ImportError: + pass + + +def lambertw_pade(): + derivs = [mpmath.diff(mpmath.lambertw, 0, n=n) for n in range(6)] + p, q = mpmath.pade(derivs, 3, 2) + return p, q + + +def main(): + print(__doc__) + with mpmath.workdps(50): + p, q = lambertw_pade() + p, q = p[::-1], q[::-1] + print(f"p = {p}") + print(f"q = {q}") + + x, y = np.linspace(-1.5, 1.5, 75), np.linspace(-1.5, 1.5, 75) + x, y = np.meshgrid(x, y) + z = x + 1j*y + lambertw_std = [] + for z0 in z.flatten(): + lambertw_std.append(complex(mpmath.lambertw(z0))) + lambertw_std = np.array(lambertw_std).reshape(x.shape) + + fig, axes = plt.subplots(nrows=3, ncols=1) + # Compare Pade approximation to true result + p = np.array([float(p0) for p0 in p]) + q = np.array([float(q0) for q0 in q]) + pade_approx = np.polyval(p, z)/np.polyval(q, z) + pade_err = abs(pade_approx - lambertw_std) + axes[0].pcolormesh(x, y, pade_err) + # Compare two terms of asymptotic series to true result + asy_approx = np.log(z) - np.log(np.log(z)) + asy_err = abs(asy_approx - lambertw_std) + axes[1].pcolormesh(x, y, asy_err) + # Compare two terms of the series around the branch point to the + # true result + p = np.sqrt(2*(np.exp(1)*z + 1)) + series_approx = -1 + p - p**2/3 + series_err = abs(series_approx - lambertw_std) + im = axes[2].pcolormesh(x, y, series_err) + + fig.colorbar(im, ax=axes.ravel().tolist()) + plt.show() + + fig, ax = plt.subplots(nrows=1, ncols=1) + pade_better = pade_err < asy_err + im = ax.pcolormesh(x, y, pade_better) + t = np.linspace(-0.3, 0.3) + ax.plot(-2.5*abs(t) - 0.2, t, 'r') + fig.colorbar(im, ax=ax) + plt.show() + + +if __name__ == '__main__': + main() diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/struve_convergence.py b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/struve_convergence.py new file mode 100644 index 0000000000000000000000000000000000000000..dbf6009368540dbf603b61f5b72510f0acd1a65b --- /dev/null +++ b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/struve_convergence.py @@ -0,0 +1,131 @@ +""" +Convergence regions of the expansions used in ``struve.c`` + +Note that for v >> z both functions tend rapidly to 0, +and for v << -z, they tend to infinity. + +The floating-point functions over/underflow in the lower left and right +corners of the figure. + + +Figure legend +============= + +Red region + Power series is close (1e-12) to the mpmath result + +Blue region + Asymptotic series is close to the mpmath result + +Green region + Bessel series is close to the mpmath result + +Dotted colored lines + Boundaries of the regions + +Solid colored lines + Boundaries estimated by the routine itself. These will be used + for determining which of the results to use. + +Black dashed line + The line z = 0.7*|v| + 12 + +""" +import numpy as np +import matplotlib.pyplot as plt + +import mpmath + + +def err_metric(a, b, atol=1e-290): + m = abs(a - b) / (atol + abs(b)) + m[np.isinf(b) & (a == b)] = 0 + return m + + +def do_plot(is_h=True): + from scipy.special._ufuncs import (_struve_power_series, + _struve_asymp_large_z, + _struve_bessel_series) + + vs = np.linspace(-1000, 1000, 91) + zs = np.sort(np.r_[1e-5, 1.0, np.linspace(0, 700, 91)[1:]]) + + rp = _struve_power_series(vs[:,None], zs[None,:], is_h) + ra = _struve_asymp_large_z(vs[:,None], zs[None,:], is_h) + rb = _struve_bessel_series(vs[:,None], zs[None,:], is_h) + + mpmath.mp.dps = 50 + if is_h: + def sh(v, z): + return float(mpmath.struveh(mpmath.mpf(v), mpmath.mpf(z))) + else: + def sh(v, z): + return float(mpmath.struvel(mpmath.mpf(v), mpmath.mpf(z))) + ex = np.vectorize(sh, otypes='d')(vs[:,None], zs[None,:]) + + err_a = err_metric(ra[0], ex) + 1e-300 + err_p = err_metric(rp[0], ex) + 1e-300 + err_b = err_metric(rb[0], ex) + 1e-300 + + err_est_a = abs(ra[1]/ra[0]) + err_est_p = abs(rp[1]/rp[0]) + err_est_b = abs(rb[1]/rb[0]) + + z_cutoff = 0.7*abs(vs) + 12 + + levels = [-1000, -12] + + plt.cla() + + plt.hold(1) + plt.contourf(vs, zs, np.log10(err_p).T, + levels=levels, colors=['r', 'r'], alpha=0.1) + plt.contourf(vs, zs, np.log10(err_a).T, + levels=levels, colors=['b', 'b'], alpha=0.1) + plt.contourf(vs, zs, np.log10(err_b).T, + levels=levels, colors=['g', 'g'], alpha=0.1) + + plt.contour(vs, zs, np.log10(err_p).T, + levels=levels, colors=['r', 'r'], linestyles=[':', ':']) + plt.contour(vs, zs, np.log10(err_a).T, + levels=levels, colors=['b', 'b'], linestyles=[':', ':']) + plt.contour(vs, zs, np.log10(err_b).T, + levels=levels, colors=['g', 'g'], linestyles=[':', ':']) + + lp = plt.contour(vs, zs, np.log10(err_est_p).T, + levels=levels, colors=['r', 'r'], linestyles=['-', '-']) + la = plt.contour(vs, zs, np.log10(err_est_a).T, + levels=levels, colors=['b', 'b'], linestyles=['-', '-']) + lb = plt.contour(vs, zs, np.log10(err_est_b).T, + levels=levels, colors=['g', 'g'], linestyles=['-', '-']) + + plt.clabel(lp, fmt={-1000: 'P', -12: 'P'}) + plt.clabel(la, fmt={-1000: 'A', -12: 'A'}) + plt.clabel(lb, fmt={-1000: 'B', -12: 'B'}) + + plt.plot(vs, z_cutoff, 'k--') + + plt.xlim(vs.min(), vs.max()) + plt.ylim(zs.min(), zs.max()) + + plt.xlabel('v') + plt.ylabel('z') + + +def main(): + plt.clf() + plt.subplot(121) + do_plot(True) + plt.title('Struve H') + + plt.subplot(122) + do_plot(False) + plt.title('Struve L') + + plt.savefig('struve_convergence.png') + plt.show() + + +if __name__ == "__main__": + main() diff --git a/parrot/lib/python3.10/site-packages/scipy/special/_precompute/zetac.py b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/zetac.py new file mode 100644 index 0000000000000000000000000000000000000000..d408b1a2fffb6872287452923fcc9394adc13a7c --- /dev/null +++ b/parrot/lib/python3.10/site-packages/scipy/special/_precompute/zetac.py @@ -0,0 +1,27 @@ +"""Compute the Taylor series for zeta(x) - 1 around x = 0.""" +try: + import mpmath +except ImportError: + pass + + +def zetac_series(N): + coeffs = [] + with mpmath.workdps(100): + coeffs.append(-1.5) + for n in range(1, N): + coeff = mpmath.diff(mpmath.zeta, 0, n)/mpmath.factorial(n) + coeffs.append(coeff) + return coeffs + + +def main(): + print(__doc__) + coeffs = zetac_series(10) + coeffs = [mpmath.nstr(x, 20, min_fixed=0, max_fixed=0) + for x in coeffs] + print("\n".join(coeffs[::-1])) + + +if __name__ == '__main__': + main() diff --git a/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/METADATA b/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..59afa527a6d523ce43c5242b3cefafe4ee6da53e --- /dev/null +++ b/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/METADATA @@ -0,0 +1,110 @@ +Metadata-Version: 2.1 +Name: braceexpand +Version: 0.1.7 +Summary: Bash-style brace expansion for Python +Home-page: https://github.com/trendels/braceexpand +Author: Stanis Trendelenburg +Author-email: stanis.trendelenburg@gmail.com +License: MIT +Platform: UNKNOWN +Classifier: License :: OSI Approved :: MIT License +Classifier: Programming Language :: Python +Classifier: Programming Language :: Python :: 2 +Classifier: Programming Language :: Python :: 3 + +Bash-style brace expansion for Python +===================================== + +|build-status-img| |PyPI| + +Implements Brace Expansion as described in +`bash(1) `__, +with the following limitations: + +- A pattern containing unbalanced braces will raise an + ``UnbalancedBracesError`` exception. In bash, unbalanced braces will + either be partly expanded or ignored. + +- A mixed-case character range like ``'{Z..a}'`` or ``'{a..Z}'`` will + not include the characters :literal:`[]^_\`` between ``Z`` and ``a``. + +``braceexpand`` is tested with Python 2.7, and 3.6+ + +Installation +------------ + +Install the ``braceexpand`` package from pypi: + +:: + + $ pip install braceexpand + +Examples +-------- + +The ``braceexpand`` function returns an iterator over the expansions +generated from a pattern. + +.. code:: python + + >>> from braceexpand import braceexpand + + # Integer range + >>> list(braceexpand('item{1..3}')) + ['item1', 'item2', 'item3'] + + # Character range + >>> list(braceexpand('{a..c}')) + ['a', 'b', 'c'] + + # Sequence + >>> list(braceexpand('index.html{,.backup}')) + ['index.html', 'index.html.backup'] + + # Nested patterns + >>> list(braceexpand('python{2.{5..7},3.{2,3}}')) + ['python2.5', 'python2.6', 'python2.7', 'python3.2', 'python3.3'] + + # Prefixing an integer with zero causes all numbers to be padded to + # the same width. + >>> list(braceexpand('{07..10}')) + ['07', '08', '09', '10'] + + # An optional increment can be specified for ranges. + >>> list(braceexpand('{a..g..2}')) + ['a', 'c', 'e', 'g'] + + # Ranges can go in both directions. + >>> list(braceexpand('{4..1}')) + ['4', '3', '2', '1'] + + # Numbers can be negative + >>> list(braceexpand('{2..-1}')) + ['2', '1', '0', '-1'] + + # Unbalanced braces raise an exception. + >>> list(braceexpand('{1{2,3}')) + Traceback (most recent call last): + ... + UnbalancedBracesError: Unbalanced braces: '{1{2,3}' + + # By default, the backslash is the escape character. + >>> list(braceexpand(r'{1\{2,3}')) + ['1{2', '3'] + + # Setting 'escape' to False disables backslash escaping. + >>> list(braceexpand(r'\{1,2}', escape=False)) + ['\\1', '\\2'] + +License +------- + +braceexpand is licensed under the MIT License. See the included file +``LICENSE`` for details. + +.. |build-status-img| image:: https://travis-ci.org/trendels/braceexpand.svg + :target: https://travis-ci.org/trendels/braceexpand +.. |PyPI| image:: https://img.shields.io/pypi/v/braceexpand + :target: https://pypi.python.org/pypi/braceexpand + + diff --git a/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/RECORD b/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..26e320dca6715f4567f432a12f4f633add65303a --- /dev/null +++ b/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/RECORD @@ -0,0 +1,11 @@ +braceexpand-0.1.7.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +braceexpand-0.1.7.dist-info/LICENSE,sha256=16k5EZk6frwA7VGnHs6aIA2pkVT-_15m_8Be-A13kVg,1087 +braceexpand-0.1.7.dist-info/METADATA,sha256=9WVJW25YMteqR_4sg3Oo6lhlVyCyj7bKku4uI88RWgI,3020 +braceexpand-0.1.7.dist-info/RECORD,, +braceexpand-0.1.7.dist-info/REQUESTED,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +braceexpand-0.1.7.dist-info/WHEEL,sha256=Z-nyYpwrcSqxfdux5Mbn_DQ525iP7J2DG3JgGvOYyTQ,110 +braceexpand-0.1.7.dist-info/top_level.txt,sha256=MAMTtw552zcB8GIoBh6gKp0KQe6XF7zLObRidLVm7FI,12 +braceexpand/__init__.py,sha256=Q9QDqLAVG7nowzGZikQN0kMfRaa3XoU6MSAR12HTNnk,6700 +braceexpand/__init__.pyi,sha256=OE8cXPKsFPayrGjYIWbV7_nNPhKzjsfNRZRIlrapZog,117 +braceexpand/__pycache__/__init__.cpython-310.pyc,, +braceexpand/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 diff --git a/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/REQUESTED b/vllm/lib/python3.10/site-packages/braceexpand-0.1.7.dist-info/REQUESTED new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vllm/lib/python3.10/site-packages/gguf/__init__.py b/vllm/lib/python3.10/site-packages/gguf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..243defc4c1ca42d3713017d8902592f54ac849cd --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/__init__.py @@ -0,0 +1,9 @@ +from .constants import * +from .lazy import * +from .gguf_reader import * +from .gguf_writer import * +from .quants import * +from .tensor_mapping import * +from .vocab import * +from .utility import * +from .metadata import * diff --git a/vllm/lib/python3.10/site-packages/gguf/constants.py b/vllm/lib/python3.10/site-packages/gguf/constants.py new file mode 100644 index 0000000000000000000000000000000000000000..b55effa9907b100106cc1fe5a88e2abbb7dd505d --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/constants.py @@ -0,0 +1,1398 @@ +from __future__ import annotations + +from enum import Enum, IntEnum, auto +from typing import Any + +# +# constants +# + +GGUF_MAGIC = 0x46554747 # "GGUF" +GGUF_VERSION = 3 +GGUF_DEFAULT_ALIGNMENT = 32 +GGML_QUANT_VERSION = 2 # GGML_QNT_VERSION from ggml.h + +# +# metadata keys +# + + +class Keys: + class General: + TYPE = "general.type" + ARCHITECTURE = "general.architecture" + QUANTIZATION_VERSION = "general.quantization_version" + ALIGNMENT = "general.alignment" + FILE_TYPE = "general.file_type" + + # Authorship Metadata + NAME = "general.name" + AUTHOR = "general.author" + VERSION = "general.version" + ORGANIZATION = "general.organization" + + FINETUNE = "general.finetune" + BASENAME = "general.basename" + + DESCRIPTION = "general.description" + QUANTIZED_BY = "general.quantized_by" + + SIZE_LABEL = "general.size_label" + + # Licensing details + LICENSE = "general.license" + LICENSE_NAME = "general.license.name" + LICENSE_LINK = "general.license.link" + + # Typically represents the converted GGUF repo (Unless native) + URL = "general.url" # Model Website/Paper + DOI = "general.doi" + UUID = "general.uuid" + REPO_URL = "general.repo_url" # Model Source Repository (git/svn/etc...) + + # Model Source during conversion + SOURCE_URL = "general.source.url" # Model Website/Paper + SOURCE_DOI = "general.source.doi" + SOURCE_UUID = "general.source.uuid" + SOURCE_REPO_URL = "general.source.repo_url" # Model Source Repository (git/svn/etc...) + + # Base Model Source. There can be more than one source if it's a merged + # model like with 'Mistral-7B-Merge-14-v0.1'. This will assist in + # tracing linage of models as it is finetuned or merged over time. + BASE_MODEL_COUNT = "general.base_model.count" + BASE_MODEL_NAME = "general.base_model.{id}.name" + BASE_MODEL_AUTHOR = "general.base_model.{id}.author" + BASE_MODEL_VERSION = "general.base_model.{id}.version" + BASE_MODEL_ORGANIZATION = "general.base_model.{id}.organization" + BASE_MODEL_URL = "general.base_model.{id}.url" # Model Website/Paper + BASE_MODEL_DOI = "general.base_model.{id}.doi" + BASE_MODEL_UUID = "general.base_model.{id}.uuid" + BASE_MODEL_REPO_URL = "general.base_model.{id}.repo_url" # Model Source Repository (git/svn/etc...) + + # Array based KV stores + TAGS = "general.tags" + LANGUAGES = "general.languages" + DATASETS = "general.datasets" + + class LLM: + VOCAB_SIZE = "{arch}.vocab_size" + CONTEXT_LENGTH = "{arch}.context_length" + EMBEDDING_LENGTH = "{arch}.embedding_length" + BLOCK_COUNT = "{arch}.block_count" + LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count" + FEED_FORWARD_LENGTH = "{arch}.feed_forward_length" + EXPERT_FEED_FORWARD_LENGTH = "{arch}.expert_feed_forward_length" + EXPERT_SHARED_FEED_FORWARD_LENGTH = "{arch}.expert_shared_feed_forward_length" + USE_PARALLEL_RESIDUAL = "{arch}.use_parallel_residual" + TENSOR_DATA_LAYOUT = "{arch}.tensor_data_layout" + EXPERT_COUNT = "{arch}.expert_count" + EXPERT_USED_COUNT = "{arch}.expert_used_count" + EXPERT_SHARED_COUNT = "{arch}.expert_shared_count" + EXPERT_WEIGHTS_SCALE = "{arch}.expert_weights_scale" + POOLING_TYPE = "{arch}.pooling_type" + LOGIT_SCALE = "{arch}.logit_scale" + DECODER_START_TOKEN_ID = "{arch}.decoder_start_token_id" + ATTN_LOGIT_SOFTCAPPING = "{arch}.attn_logit_softcapping" + FINAL_LOGIT_SOFTCAPPING = "{arch}.final_logit_softcapping" + + class Attention: + HEAD_COUNT = "{arch}.attention.head_count" + HEAD_COUNT_KV = "{arch}.attention.head_count_kv" + MAX_ALIBI_BIAS = "{arch}.attention.max_alibi_bias" + CLAMP_KQV = "{arch}.attention.clamp_kqv" + KEY_LENGTH = "{arch}.attention.key_length" + VALUE_LENGTH = "{arch}.attention.value_length" + LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon" + LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon" + CAUSAL = "{arch}.attention.causal" + Q_LORA_RANK = "{arch}.attention.q_lora_rank" + KV_LORA_RANK = "{arch}.attention.kv_lora_rank" + REL_BUCKETS_COUNT = "{arch}.attention.relative_buckets_count" + SLIDING_WINDOW = "{arch}.attention.sliding_window" + + class Rope: + DIMENSION_COUNT = "{arch}.rope.dimension_count" + FREQ_BASE = "{arch}.rope.freq_base" + SCALING_TYPE = "{arch}.rope.scaling.type" + SCALING_FACTOR = "{arch}.rope.scaling.factor" + SCALING_ATTN_FACTOR = "{arch}.rope.scaling.attn_factor" + SCALING_ORIG_CTX_LEN = "{arch}.rope.scaling.original_context_length" + SCALING_FINETUNED = "{arch}.rope.scaling.finetuned" + SCALING_YARN_LOG_MUL = "{arch}.rope.scaling.yarn_log_multiplier" + + class Split: + LLM_KV_SPLIT_NO = "split.no" + LLM_KV_SPLIT_COUNT = "split.count" + LLM_KV_SPLIT_TENSORS_COUNT = "split.tensors.count" + + class SSM: + CONV_KERNEL = "{arch}.ssm.conv_kernel" + INNER_SIZE = "{arch}.ssm.inner_size" + STATE_SIZE = "{arch}.ssm.state_size" + TIME_STEP_RANK = "{arch}.ssm.time_step_rank" + DT_B_C_RMS = "{arch}.ssm.dt_b_c_rms" + + class Tokenizer: + MODEL = "tokenizer.ggml.model" + PRE = "tokenizer.ggml.pre" + LIST = "tokenizer.ggml.tokens" + TOKEN_TYPE = "tokenizer.ggml.token_type" + TOKEN_TYPE_COUNT = "tokenizer.ggml.token_type_count" # for BERT-style token types + SCORES = "tokenizer.ggml.scores" + MERGES = "tokenizer.ggml.merges" + BOS_ID = "tokenizer.ggml.bos_token_id" + EOS_ID = "tokenizer.ggml.eos_token_id" + UNK_ID = "tokenizer.ggml.unknown_token_id" + SEP_ID = "tokenizer.ggml.seperator_token_id" + PAD_ID = "tokenizer.ggml.padding_token_id" + CLS_ID = "tokenizer.ggml.cls_token_id" + MASK_ID = "tokenizer.ggml.mask_token_id" + ADD_BOS = "tokenizer.ggml.add_bos_token" + ADD_EOS = "tokenizer.ggml.add_eos_token" + ADD_PREFIX = "tokenizer.ggml.add_space_prefix" + REMOVE_EXTRA_WS = "tokenizer.ggml.remove_extra_whitespaces" + PRECOMPILED_CHARSMAP = "tokenizer.ggml.precompiled_charsmap" + HF_JSON = "tokenizer.huggingface.json" + RWKV = "tokenizer.rwkv.world" + CHAT_TEMPLATE = "tokenizer.chat_template" + CHAT_TEMPLATE_N = "tokenizer.chat_template.{name}" + CHAT_TEMPLATES = "tokenizer.chat_templates" + # FIM/Infill special tokens constants + PREFIX_ID = "tokenizer.ggml.prefix_token_id" + SUFFIX_ID = "tokenizer.ggml.suffix_token_id" + MIDDLE_ID = "tokenizer.ggml.middle_token_id" + EOT_ID = "tokenizer.ggml.eot_token_id" + EOM_ID = "tokenizer.ggml.eom_token_id" + + class Adapter: + TYPE = "adapter.type" + LORA_ALPHA = "adapter.lora.alpha" + +# +# recommended mapping of model tensor names for storage in gguf +# + + +class GGUFType: + MODEL = "model" + ADAPTER = "adapter" + + +class MODEL_ARCH(IntEnum): + LLAMA = auto() + FALCON = auto() + BAICHUAN = auto() + GROK = auto() + GPT2 = auto() + GPTJ = auto() + GPTNEOX = auto() + MPT = auto() + STARCODER = auto() + REFACT = auto() + BERT = auto() + NOMIC_BERT = auto() + JINA_BERT_V2 = auto() + BLOOM = auto() + STABLELM = auto() + QWEN = auto() + QWEN2 = auto() + QWEN2MOE = auto() + PHI2 = auto() + PHI3 = auto() + PLAMO = auto() + CODESHELL = auto() + ORION = auto() + INTERNLM2 = auto() + MINICPM = auto() + GEMMA = auto() + GEMMA2 = auto() + STARCODER2 = auto() + MAMBA = auto() + XVERSE = auto() + COMMAND_R = auto() + DBRX = auto() + OLMO = auto() + OPENELM = auto() + ARCTIC = auto() + DEEPSEEK2 = auto() + CHATGLM = auto() + BITNET = auto() + T5 = auto() + T5ENCODER = auto() + JAIS = auto() + NEMOTRON = auto() + EXAONE = auto() + + +class MODEL_TENSOR(IntEnum): + TOKEN_EMBD = auto() + TOKEN_EMBD_NORM = auto() + TOKEN_TYPES = auto() + POS_EMBD = auto() + OUTPUT = auto() + OUTPUT_NORM = auto() + ROPE_FREQS = auto() + ROPE_FACTORS_LONG = auto() + ROPE_FACTORS_SHORT = auto() + ATTN_Q = auto() + ATTN_K = auto() + ATTN_V = auto() + ATTN_QKV = auto() + ATTN_OUT = auto() + ATTN_NORM = auto() + ATTN_NORM_2 = auto() + ATTN_OUT_NORM = auto() + ATTN_POST_NORM = auto() + ATTN_ROT_EMBD = auto() + FFN_GATE_INP = auto() + FFN_GATE_INP_SHEXP = auto() + FFN_NORM = auto() + FFN_PRE_NORM = auto() + FFN_POST_NORM = auto() + FFN_GATE = auto() + FFN_DOWN = auto() + FFN_UP = auto() + FFN_ACT = auto() + FFN_NORM_EXP = auto() + FFN_GATE_EXP = auto() + FFN_DOWN_EXP = auto() + FFN_UP_EXP = auto() + FFN_GATE_SHEXP = auto() + FFN_DOWN_SHEXP = auto() + FFN_UP_SHEXP = auto() + ATTN_Q_NORM = auto() + ATTN_K_NORM = auto() + LAYER_OUT_NORM = auto() + SSM_IN = auto() + SSM_CONV1D = auto() + SSM_X = auto() + SSM_DT = auto() + SSM_A = auto() + SSM_D = auto() + SSM_OUT = auto() + ATTN_Q_A = auto() + ATTN_Q_B = auto() + ATTN_KV_A_MQA = auto() + ATTN_KV_B = auto() + ATTN_Q_A_NORM = auto() + ATTN_KV_A_NORM = auto() + FFN_SUB_NORM = auto() + ATTN_SUB_NORM = auto() + DEC_ATTN_NORM = auto() + DEC_ATTN_Q = auto() + DEC_ATTN_K = auto() + DEC_ATTN_V = auto() + DEC_ATTN_OUT = auto() + DEC_ATTN_REL_B = auto() + DEC_CROSS_ATTN_NORM = auto() + DEC_CROSS_ATTN_Q = auto() + DEC_CROSS_ATTN_K = auto() + DEC_CROSS_ATTN_V = auto() + DEC_CROSS_ATTN_OUT = auto() + DEC_CROSS_ATTN_REL_B = auto() + DEC_FFN_NORM = auto() + DEC_FFN_GATE = auto() + DEC_FFN_DOWN = auto() + DEC_FFN_UP = auto() + DEC_OUTPUT_NORM = auto() + ENC_ATTN_NORM = auto() + ENC_ATTN_Q = auto() + ENC_ATTN_K = auto() + ENC_ATTN_V = auto() + ENC_ATTN_OUT = auto() + ENC_ATTN_REL_B = auto() + ENC_FFN_NORM = auto() + ENC_FFN_GATE = auto() + ENC_FFN_DOWN = auto() + ENC_FFN_UP = auto() + ENC_OUTPUT_NORM = auto() + + +MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = { + MODEL_ARCH.LLAMA: "llama", + MODEL_ARCH.FALCON: "falcon", + MODEL_ARCH.BAICHUAN: "baichuan", + MODEL_ARCH.GROK: "grok", + MODEL_ARCH.GPT2: "gpt2", + MODEL_ARCH.GPTJ: "gptj", + MODEL_ARCH.GPTNEOX: "gptneox", + MODEL_ARCH.MPT: "mpt", + MODEL_ARCH.STARCODER: "starcoder", + MODEL_ARCH.REFACT: "refact", + MODEL_ARCH.BERT: "bert", + MODEL_ARCH.NOMIC_BERT: "nomic-bert", + MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2", + MODEL_ARCH.BLOOM: "bloom", + MODEL_ARCH.STABLELM: "stablelm", + MODEL_ARCH.QWEN: "qwen", + MODEL_ARCH.QWEN2: "qwen2", + MODEL_ARCH.QWEN2MOE: "qwen2moe", + MODEL_ARCH.PHI2: "phi2", + MODEL_ARCH.PHI3: "phi3", + MODEL_ARCH.PLAMO: "plamo", + MODEL_ARCH.CODESHELL: "codeshell", + MODEL_ARCH.ORION: "orion", + MODEL_ARCH.INTERNLM2: "internlm2", + MODEL_ARCH.MINICPM: "minicpm", + MODEL_ARCH.GEMMA: "gemma", + MODEL_ARCH.GEMMA2: "gemma2", + MODEL_ARCH.STARCODER2: "starcoder2", + MODEL_ARCH.MAMBA: "mamba", + MODEL_ARCH.XVERSE: "xverse", + MODEL_ARCH.COMMAND_R: "command-r", + MODEL_ARCH.DBRX: "dbrx", + MODEL_ARCH.OLMO: "olmo", + MODEL_ARCH.OPENELM: "openelm", + MODEL_ARCH.ARCTIC: "arctic", + MODEL_ARCH.DEEPSEEK2: "deepseek2", + MODEL_ARCH.CHATGLM: "chatglm", + MODEL_ARCH.BITNET: "bitnet", + MODEL_ARCH.T5: "t5", + MODEL_ARCH.T5ENCODER: "t5encoder", + MODEL_ARCH.JAIS: "jais", + MODEL_ARCH.NEMOTRON: "nemotron", + MODEL_ARCH.EXAONE: "exaone", +} + +TENSOR_NAMES: dict[MODEL_TENSOR, str] = { + MODEL_TENSOR.TOKEN_EMBD: "token_embd", + MODEL_TENSOR.TOKEN_EMBD_NORM: "token_embd_norm", + MODEL_TENSOR.TOKEN_TYPES: "token_types", + MODEL_TENSOR.POS_EMBD: "position_embd", + MODEL_TENSOR.OUTPUT_NORM: "output_norm", + MODEL_TENSOR.OUTPUT: "output", + MODEL_TENSOR.ROPE_FREQS: "rope_freqs", + MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long", + MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short", + MODEL_TENSOR.ATTN_NORM: "blk.{bid}.attn_norm", + MODEL_TENSOR.ATTN_NORM_2: "blk.{bid}.attn_norm_2", + MODEL_TENSOR.ATTN_QKV: "blk.{bid}.attn_qkv", + MODEL_TENSOR.ATTN_Q: "blk.{bid}.attn_q", + MODEL_TENSOR.ATTN_K: "blk.{bid}.attn_k", + MODEL_TENSOR.ATTN_V: "blk.{bid}.attn_v", + MODEL_TENSOR.ATTN_OUT: "blk.{bid}.attn_output", + MODEL_TENSOR.ATTN_ROT_EMBD: "blk.{bid}.attn_rot_embd", + MODEL_TENSOR.ATTN_Q_NORM: "blk.{bid}.attn_q_norm", + MODEL_TENSOR.ATTN_K_NORM: "blk.{bid}.attn_k_norm", + MODEL_TENSOR.ATTN_OUT_NORM: "blk.{bid}.attn_output_norm", + MODEL_TENSOR.ATTN_POST_NORM: "blk.{bid}.post_attention_norm", + MODEL_TENSOR.FFN_GATE_INP: "blk.{bid}.ffn_gate_inp", + MODEL_TENSOR.FFN_GATE_INP_SHEXP: "blk.{bid}.ffn_gate_inp_shexp", + MODEL_TENSOR.FFN_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_PRE_NORM: "blk.{bid}.ffn_norm", + MODEL_TENSOR.FFN_POST_NORM: "blk.{bid}.post_ffw_norm", + MODEL_TENSOR.FFN_GATE: "blk.{bid}.ffn_gate", + MODEL_TENSOR.FFN_DOWN: "blk.{bid}.ffn_down", + MODEL_TENSOR.FFN_UP: "blk.{bid}.ffn_up", + MODEL_TENSOR.FFN_GATE_SHEXP: "blk.{bid}.ffn_gate_shexp", + MODEL_TENSOR.FFN_DOWN_SHEXP: "blk.{bid}.ffn_down_shexp", + MODEL_TENSOR.FFN_UP_SHEXP: "blk.{bid}.ffn_up_shexp", + MODEL_TENSOR.FFN_ACT: "blk.{bid}.ffn", + MODEL_TENSOR.FFN_NORM_EXP: "blk.{bid}.ffn_norm_exps", + MODEL_TENSOR.FFN_GATE_EXP: "blk.{bid}.ffn_gate_exps", + MODEL_TENSOR.FFN_DOWN_EXP: "blk.{bid}.ffn_down_exps", + MODEL_TENSOR.FFN_UP_EXP: "blk.{bid}.ffn_up_exps", + MODEL_TENSOR.LAYER_OUT_NORM: "blk.{bid}.layer_output_norm", + MODEL_TENSOR.SSM_IN: "blk.{bid}.ssm_in", + MODEL_TENSOR.SSM_CONV1D: "blk.{bid}.ssm_conv1d", + MODEL_TENSOR.SSM_X: "blk.{bid}.ssm_x", + MODEL_TENSOR.SSM_DT: "blk.{bid}.ssm_dt", + MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a", + MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d", + MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out", + MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a", + MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b", + MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa", + MODEL_TENSOR.ATTN_KV_B: "blk.{bid}.attn_kv_b", + MODEL_TENSOR.ATTN_Q_A_NORM: "blk.{bid}.attn_q_a_norm", + MODEL_TENSOR.ATTN_KV_A_NORM: "blk.{bid}.attn_kv_a_norm", + MODEL_TENSOR.ATTN_SUB_NORM: "blk.{bid}.attn_sub_norm", + MODEL_TENSOR.FFN_SUB_NORM: "blk.{bid}.ffn_sub_norm", + MODEL_TENSOR.DEC_ATTN_NORM: "dec.blk.{bid}.attn_norm", + MODEL_TENSOR.DEC_ATTN_Q: "dec.blk.{bid}.attn_q", + MODEL_TENSOR.DEC_ATTN_K: "dec.blk.{bid}.attn_k", + MODEL_TENSOR.DEC_ATTN_V: "dec.blk.{bid}.attn_v", + MODEL_TENSOR.DEC_ATTN_OUT: "dec.blk.{bid}.attn_o", + MODEL_TENSOR.DEC_ATTN_REL_B: "dec.blk.{bid}.attn_rel_b", + MODEL_TENSOR.DEC_CROSS_ATTN_NORM: "dec.blk.{bid}.cross_attn_norm", + MODEL_TENSOR.DEC_CROSS_ATTN_Q: "dec.blk.{bid}.cross_attn_q", + MODEL_TENSOR.DEC_CROSS_ATTN_K: "dec.blk.{bid}.cross_attn_k", + MODEL_TENSOR.DEC_CROSS_ATTN_V: "dec.blk.{bid}.cross_attn_v", + MODEL_TENSOR.DEC_CROSS_ATTN_OUT: "dec.blk.{bid}.cross_attn_o", + MODEL_TENSOR.DEC_CROSS_ATTN_REL_B: "dec.blk.{bid}.cross_attn_rel_b", + MODEL_TENSOR.DEC_FFN_NORM: "dec.blk.{bid}.ffn_norm", + MODEL_TENSOR.DEC_FFN_GATE: "dec.blk.{bid}.ffn_gate", + MODEL_TENSOR.DEC_FFN_DOWN: "dec.blk.{bid}.ffn_down", + MODEL_TENSOR.DEC_FFN_UP: "dec.blk.{bid}.ffn_up", + MODEL_TENSOR.DEC_OUTPUT_NORM: "dec.output_norm", + MODEL_TENSOR.ENC_ATTN_NORM: "enc.blk.{bid}.attn_norm", + MODEL_TENSOR.ENC_ATTN_Q: "enc.blk.{bid}.attn_q", + MODEL_TENSOR.ENC_ATTN_K: "enc.blk.{bid}.attn_k", + MODEL_TENSOR.ENC_ATTN_V: "enc.blk.{bid}.attn_v", + MODEL_TENSOR.ENC_ATTN_OUT: "enc.blk.{bid}.attn_o", + MODEL_TENSOR.ENC_ATTN_REL_B: "enc.blk.{bid}.attn_rel_b", + MODEL_TENSOR.ENC_FFN_NORM: "enc.blk.{bid}.ffn_norm", + MODEL_TENSOR.ENC_FFN_GATE: "enc.blk.{bid}.ffn_gate", + MODEL_TENSOR.ENC_FFN_DOWN: "enc.blk.{bid}.ffn_down", + MODEL_TENSOR.ENC_FFN_UP: "enc.blk.{bid}.ffn_up", + MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm", +} + +MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.GROK: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.GPTNEOX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.FALCON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STARCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.NOMIC_BERT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.JINA_BERT_V2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.TOKEN_TYPES, + MODEL_TENSOR.ATTN_NORM_2, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.LAYER_OUT_NORM, + ], + MODEL_ARCH.MPT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_ACT, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.POS_EMBD, + ], + MODEL_ARCH.GPTJ: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.REFACT: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BLOOM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.TOKEN_EMBD_NORM, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.STABLELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + ], + MODEL_ARCH.QWEN: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.QWEN2MOE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_INP_SHEXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.PLAMO: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.GPT2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PHI2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.PHI3: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.CODESHELL: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.POS_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.ORION: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.INTERNLM2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MINICPM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.GEMMA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM, + ], + MODEL_ARCH.GEMMA2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_POST_NORM, + MODEL_TENSOR.FFN_PRE_NORM, + MODEL_TENSOR.FFN_POST_NORM, + ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.MAMBA: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.SSM_IN, + MODEL_TENSOR.SSM_CONV1D, + MODEL_TENSOR.SSM_X, + MODEL_TENSOR.SSM_DT, + MODEL_TENSOR.SSM_A, + MODEL_TENSOR.SSM_D, + MODEL_TENSOR.SSM_OUT, + ], + MODEL_ARCH.XVERSE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.COMMAND_R: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_Q_NORM, + ], + MODEL_ARCH.DBRX: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_OUT_NORM, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.OLMO: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.OPENELM: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_Q_NORM, + MODEL_TENSOR.ATTN_K_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.ARCTIC: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_NORM_EXP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_Q_A, + MODEL_TENSOR.ATTN_Q_B, + MODEL_TENSOR.ATTN_KV_A_MQA, + MODEL_TENSOR.ATTN_KV_B, + MODEL_TENSOR.ATTN_Q_A_NORM, + MODEL_TENSOR.ATTN_KV_A_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_GATE_INP, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.FFN_GATE_EXP, + MODEL_TENSOR.FFN_DOWN_EXP, + MODEL_TENSOR.FFN_UP_EXP, + MODEL_TENSOR.FFN_GATE_SHEXP, + MODEL_TENSOR.FFN_DOWN_SHEXP, + MODEL_TENSOR.FFN_UP_SHEXP, + ], + MODEL_ARCH.CHATGLM : [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.BITNET: [ + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + MODEL_TENSOR.ATTN_SUB_NORM, + MODEL_TENSOR.FFN_SUB_NORM, + ], + MODEL_ARCH.T5: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.DEC_ATTN_NORM, + MODEL_TENSOR.DEC_ATTN_Q, + MODEL_TENSOR.DEC_ATTN_K, + MODEL_TENSOR.DEC_ATTN_V, + MODEL_TENSOR.DEC_ATTN_OUT, + MODEL_TENSOR.DEC_ATTN_REL_B, + MODEL_TENSOR.DEC_CROSS_ATTN_NORM, + MODEL_TENSOR.DEC_CROSS_ATTN_Q, + MODEL_TENSOR.DEC_CROSS_ATTN_K, + MODEL_TENSOR.DEC_CROSS_ATTN_V, + MODEL_TENSOR.DEC_CROSS_ATTN_OUT, + MODEL_TENSOR.DEC_CROSS_ATTN_REL_B, + MODEL_TENSOR.DEC_FFN_NORM, + MODEL_TENSOR.DEC_FFN_GATE, + MODEL_TENSOR.DEC_FFN_DOWN, + MODEL_TENSOR.DEC_FFN_UP, + MODEL_TENSOR.DEC_OUTPUT_NORM, + MODEL_TENSOR.ENC_ATTN_NORM, + MODEL_TENSOR.ENC_ATTN_Q, + MODEL_TENSOR.ENC_ATTN_K, + MODEL_TENSOR.ENC_ATTN_V, + MODEL_TENSOR.ENC_ATTN_OUT, + MODEL_TENSOR.ENC_ATTN_REL_B, + MODEL_TENSOR.ENC_FFN_NORM, + MODEL_TENSOR.ENC_FFN_GATE, + MODEL_TENSOR.ENC_FFN_DOWN, + MODEL_TENSOR.ENC_FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + ], + MODEL_ARCH.T5ENCODER: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ENC_ATTN_NORM, + MODEL_TENSOR.ENC_ATTN_Q, + MODEL_TENSOR.ENC_ATTN_K, + MODEL_TENSOR.ENC_ATTN_V, + MODEL_TENSOR.ENC_ATTN_OUT, + MODEL_TENSOR.ENC_ATTN_REL_B, + MODEL_TENSOR.ENC_FFN_NORM, + MODEL_TENSOR.ENC_FFN_GATE, + MODEL_TENSOR.ENC_FFN_DOWN, + MODEL_TENSOR.ENC_FFN_UP, + MODEL_TENSOR.ENC_OUTPUT_NORM, + ], + MODEL_ARCH.JAIS: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_QKV, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.NEMOTRON: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + MODEL_ARCH.EXAONE: [ + MODEL_TENSOR.TOKEN_EMBD, + MODEL_TENSOR.OUTPUT_NORM, + MODEL_TENSOR.OUTPUT, + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_NORM, + MODEL_TENSOR.ATTN_Q, + MODEL_TENSOR.ATTN_K, + MODEL_TENSOR.ATTN_V, + MODEL_TENSOR.ATTN_OUT, + MODEL_TENSOR.ATTN_ROT_EMBD, + MODEL_TENSOR.FFN_NORM, + MODEL_TENSOR.FFN_GATE, + MODEL_TENSOR.FFN_DOWN, + MODEL_TENSOR.FFN_UP, + ], + # TODO +} + +# tensors that will not be serialized +MODEL_TENSOR_SKIP: dict[MODEL_ARCH, list[MODEL_TENSOR]] = { + MODEL_ARCH.LLAMA: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.BAICHUAN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.QWEN: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.CODESHELL: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.ORION: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.STARCODER2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.XVERSE: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.DEEPSEEK2: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], + MODEL_ARCH.CHATGLM: [ + MODEL_TENSOR.ROPE_FREQS, + ], + MODEL_ARCH.NEMOTRON: [ + MODEL_TENSOR.ROPE_FREQS, + MODEL_TENSOR.ATTN_ROT_EMBD, + ], +} + +# +# types +# + + +class TokenType(IntEnum): + NORMAL = 1 + UNKNOWN = 2 + CONTROL = 3 + USER_DEFINED = 4 + UNUSED = 5 + BYTE = 6 + + +class RopeScalingType(Enum): + NONE = 'none' + LINEAR = 'linear' + YARN = 'yarn' + + +class PoolingType(IntEnum): + NONE = 0 + MEAN = 1 + CLS = 2 + + +class GGMLQuantizationType(IntEnum): + F32 = 0 + F16 = 1 + Q4_0 = 2 + Q4_1 = 3 + Q5_0 = 6 + Q5_1 = 7 + Q8_0 = 8 + Q8_1 = 9 + Q2_K = 10 + Q3_K = 11 + Q4_K = 12 + Q5_K = 13 + Q6_K = 14 + Q8_K = 15 + IQ2_XXS = 16 + IQ2_XS = 17 + IQ3_XXS = 18 + IQ1_S = 19 + IQ4_NL = 20 + IQ3_S = 21 + IQ2_S = 22 + IQ4_XS = 23 + I8 = 24 + I16 = 25 + I32 = 26 + I64 = 27 + F64 = 28 + IQ1_M = 29 + BF16 = 30 + Q4_0_4_4 = 31 + Q4_0_4_8 = 32 + Q4_0_8_8 = 33 + + +# TODO: add GGMLFileType from ggml_ftype in ggml.h + + +# from llama_ftype in llama.h +# ALL VALUES SHOULD BE THE SAME HERE AS THEY ARE OVER THERE. +class LlamaFileType(IntEnum): + ALL_F32 = 0 + MOSTLY_F16 = 1 # except 1d tensors + MOSTLY_Q4_0 = 2 # except 1d tensors + MOSTLY_Q4_1 = 3 # except 1d tensors + # MOSTLY_Q4_1_SOME_F16 = 4 # tok_embeddings.weight and output.weight are F16 + # MOSTLY_Q4_2 = 5 # support has been removed + # MOSTLY_Q4_3 = 6 # support has been removed + MOSTLY_Q8_0 = 7 # except 1d tensors + MOSTLY_Q5_0 = 8 # except 1d tensors + MOSTLY_Q5_1 = 9 # except 1d tensors + MOSTLY_Q2_K = 10 # except 1d tensors + MOSTLY_Q3_K_S = 11 # except 1d tensors + MOSTLY_Q3_K_M = 12 # except 1d tensors + MOSTLY_Q3_K_L = 13 # except 1d tensors + MOSTLY_Q4_K_S = 14 # except 1d tensors + MOSTLY_Q4_K_M = 15 # except 1d tensors + MOSTLY_Q5_K_S = 16 # except 1d tensors + MOSTLY_Q5_K_M = 17 # except 1d tensors + MOSTLY_Q6_K = 18 # except 1d tensors + MOSTLY_IQ2_XXS = 19 # except 1d tensors + MOSTLY_IQ2_XS = 20 # except 1d tensors + MOSTLY_Q2_K_S = 21 # except 1d tensors + MOSTLY_IQ3_XS = 22 # except 1d tensors + MOSTLY_IQ3_XXS = 23 # except 1d tensors + MOSTLY_IQ1_S = 24 # except 1d tensors + MOSTLY_IQ4_NL = 25 # except 1d tensors + MOSTLY_IQ3_S = 26 # except 1d tensors + MOSTLY_IQ3_M = 27 # except 1d tensors + MOSTLY_IQ2_S = 28 # except 1d tensors + MOSTLY_IQ2_M = 29 # except 1d tensors + MOSTLY_IQ4_XS = 30 # except 1d tensors + MOSTLY_IQ1_M = 31 # except 1d tensors + MOSTLY_BF16 = 32 # except 1d tensors + MOSTLY_Q4_0_4_4 = 33 # except 1d tensors + MOSTLY_Q4_0_4_8 = 34 # except 1d tensors + MOSTLY_Q4_0_8_8 = 35 # except 1d tensors + + GUESSED = 1024 # not specified in the model file + + +class GGUFEndian(IntEnum): + LITTLE = 0 + BIG = 1 + + +class GGUFValueType(IntEnum): + UINT8 = 0 + INT8 = 1 + UINT16 = 2 + INT16 = 3 + UINT32 = 4 + INT32 = 5 + FLOAT32 = 6 + BOOL = 7 + STRING = 8 + ARRAY = 9 + UINT64 = 10 + INT64 = 11 + FLOAT64 = 12 + + @staticmethod + def get_type(val: Any) -> GGUFValueType: + if isinstance(val, (str, bytes, bytearray)): + return GGUFValueType.STRING + elif isinstance(val, list): + return GGUFValueType.ARRAY + elif isinstance(val, float): + return GGUFValueType.FLOAT32 + elif isinstance(val, bool): + return GGUFValueType.BOOL + elif isinstance(val, int): + return GGUFValueType.INT32 + # TODO: need help with 64-bit types in Python + else: + raise ValueError(f"Unknown type: {type(val)}") + + +# Items here are (block size, type size) +QK_K = 256 +GGML_QUANT_SIZES: dict[GGMLQuantizationType, tuple[int, int]] = { + GGMLQuantizationType.F32: (1, 4), + GGMLQuantizationType.F16: (1, 2), + GGMLQuantizationType.Q4_0: (32, 2 + 16), + GGMLQuantizationType.Q4_1: (32, 2 + 2 + 16), + GGMLQuantizationType.Q5_0: (32, 2 + 4 + 16), + GGMLQuantizationType.Q5_1: (32, 2 + 2 + 4 + 16), + GGMLQuantizationType.Q8_0: (32, 2 + 32), + GGMLQuantizationType.Q8_1: (32, 4 + 4 + 32), + GGMLQuantizationType.Q2_K: (256, 2 + 2 + QK_K // 16 + QK_K // 4), + GGMLQuantizationType.Q3_K: (256, 2 + QK_K // 4 + QK_K // 8 + 12), + GGMLQuantizationType.Q4_K: (256, 2 + 2 + QK_K // 2 + 12), + GGMLQuantizationType.Q5_K: (256, 2 + 2 + QK_K // 2 + QK_K // 8 + 12), + GGMLQuantizationType.Q6_K: (256, 2 + QK_K // 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.Q8_K: (256, 4 + QK_K + QK_K // 8), + GGMLQuantizationType.IQ2_XXS: (256, 2 + QK_K // 4), + GGMLQuantizationType.IQ2_XS: (256, 2 + QK_K // 4 + QK_K // 32), + GGMLQuantizationType.IQ3_XXS: (256, 2 + QK_K // 4 + QK_K // 8), + GGMLQuantizationType.IQ1_S: (256, 2 + QK_K // 8 + QK_K // 16), + GGMLQuantizationType.IQ4_NL: (32, 2 + 16), + GGMLQuantizationType.IQ3_S: (256, 2 + QK_K // 4 + QK_K // 8 + QK_K // 32 + 4), + GGMLQuantizationType.IQ2_S: (256, 2 + QK_K // 4 + QK_K // 16), + GGMLQuantizationType.IQ4_XS: (256, 2 + 2 + QK_K // 2 + QK_K // 64), + GGMLQuantizationType.I8: (1, 1), + GGMLQuantizationType.I16: (1, 2), + GGMLQuantizationType.I32: (1, 4), + GGMLQuantizationType.I64: (1, 8), + GGMLQuantizationType.F64: (1, 8), + GGMLQuantizationType.IQ1_M: (256, QK_K // 8 + QK_K // 16 + QK_K // 32), + GGMLQuantizationType.BF16: (1, 2), + GGMLQuantizationType.Q4_0_4_4:(32, 2 + 16), + GGMLQuantizationType.Q4_0_4_8:(32, 2 + 16), + GGMLQuantizationType.Q4_0_8_8:(32, 2 + 16), +} + + +# Aliases for backward compatibility. + +# general +KEY_GENERAL_ARCHITECTURE = Keys.General.ARCHITECTURE +KEY_GENERAL_QUANTIZATION_VERSION = Keys.General.QUANTIZATION_VERSION +KEY_GENERAL_ALIGNMENT = Keys.General.ALIGNMENT +KEY_GENERAL_NAME = Keys.General.NAME +KEY_GENERAL_AUTHOR = Keys.General.AUTHOR +KEY_GENERAL_URL = Keys.General.URL +KEY_GENERAL_DESCRIPTION = Keys.General.DESCRIPTION +KEY_GENERAL_LICENSE = Keys.General.LICENSE +KEY_GENERAL_SOURCE_URL = Keys.General.SOURCE_URL +KEY_GENERAL_FILE_TYPE = Keys.General.FILE_TYPE + +# LLM +KEY_VOCAB_SIZE = Keys.LLM.VOCAB_SIZE +KEY_CONTEXT_LENGTH = Keys.LLM.CONTEXT_LENGTH +KEY_EMBEDDING_LENGTH = Keys.LLM.EMBEDDING_LENGTH +KEY_BLOCK_COUNT = Keys.LLM.BLOCK_COUNT +KEY_FEED_FORWARD_LENGTH = Keys.LLM.FEED_FORWARD_LENGTH +KEY_USE_PARALLEL_RESIDUAL = Keys.LLM.USE_PARALLEL_RESIDUAL +KEY_TENSOR_DATA_LAYOUT = Keys.LLM.TENSOR_DATA_LAYOUT + +# attention +KEY_ATTENTION_HEAD_COUNT = Keys.Attention.HEAD_COUNT +KEY_ATTENTION_HEAD_COUNT_KV = Keys.Attention.HEAD_COUNT_KV +KEY_ATTENTION_MAX_ALIBI_BIAS = Keys.Attention.MAX_ALIBI_BIAS +KEY_ATTENTION_CLAMP_KQV = Keys.Attention.CLAMP_KQV +KEY_ATTENTION_LAYERNORM_EPS = Keys.Attention.LAYERNORM_EPS +KEY_ATTENTION_LAYERNORM_RMS_EPS = Keys.Attention.LAYERNORM_RMS_EPS + +# RoPE +KEY_ROPE_DIMENSION_COUNT = Keys.Rope.DIMENSION_COUNT +KEY_ROPE_FREQ_BASE = Keys.Rope.FREQ_BASE +KEY_ROPE_SCALING_TYPE = Keys.Rope.SCALING_TYPE +KEY_ROPE_SCALING_FACTOR = Keys.Rope.SCALING_FACTOR +KEY_ROPE_SCALING_ORIG_CTX_LEN = Keys.Rope.SCALING_ORIG_CTX_LEN +KEY_ROPE_SCALING_FINETUNED = Keys.Rope.SCALING_FINETUNED + +# SSM +KEY_SSM_CONV_KERNEL = Keys.SSM.CONV_KERNEL +KEY_SSM_INNER_SIZE = Keys.SSM.INNER_SIZE +KEY_SSM_STATE_SIZE = Keys.SSM.STATE_SIZE +KEY_SSM_TIME_STEP_RANK = Keys.SSM.TIME_STEP_RANK +KEY_SSM_DT_B_C_RMS = Keys.SSM.DT_B_C_RMS + +# tokenization +KEY_TOKENIZER_MODEL = Keys.Tokenizer.MODEL +KEY_TOKENIZER_PRE = Keys.Tokenizer.PRE +KEY_TOKENIZER_LIST = Keys.Tokenizer.LIST +KEY_TOKENIZER_TOKEN_TYPE = Keys.Tokenizer.TOKEN_TYPE +KEY_TOKENIZER_SCORES = Keys.Tokenizer.SCORES +KEY_TOKENIZER_MERGES = Keys.Tokenizer.MERGES +KEY_TOKENIZER_BOS_ID = Keys.Tokenizer.BOS_ID +KEY_TOKENIZER_EOS_ID = Keys.Tokenizer.EOS_ID +KEY_TOKENIZER_UNK_ID = Keys.Tokenizer.UNK_ID +KEY_TOKENIZER_SEP_ID = Keys.Tokenizer.SEP_ID +KEY_TOKENIZER_PAD_ID = Keys.Tokenizer.PAD_ID +KEY_TOKENIZER_CLS_ID = Keys.Tokenizer.CLS_ID +KEY_TOKENIZER_MASK_ID = Keys.Tokenizer.MASK_ID +KEY_TOKENIZER_HF_JSON = Keys.Tokenizer.HF_JSON +KEY_TOKENIZER_RWKV = Keys.Tokenizer.RWKV +KEY_TOKENIZER_PRIFIX_ID = Keys.Tokenizer.PREFIX_ID +KEY_TOKENIZER_SUFFIX_ID = Keys.Tokenizer.SUFFIX_ID +KEY_TOKENIZER_MIDDLE_ID = Keys.Tokenizer.MIDDLE_ID +KEY_TOKENIZER_EOT_ID = Keys.Tokenizer.EOT_ID +KEY_TOKENIZER_EOM_ID = Keys.Tokenizer.EOM_ID diff --git a/vllm/lib/python3.10/site-packages/gguf/gguf.py b/vllm/lib/python3.10/site-packages/gguf/gguf.py new file mode 100644 index 0000000000000000000000000000000000000000..651a81eb828248728f854c85c1a437b52892f275 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/gguf.py @@ -0,0 +1,15 @@ +# This file left for compatibility. If you want to use the GGUF API from Python +# then don't import gguf/gguf.py directly. If you're looking for examples, see the +# examples/ directory for gguf-py + +import importlib +import sys +from pathlib import Path + +sys.path.insert(0, str(Path(__file__).parent.parent)) + +# Compatibility for people trying to import gguf/gguf.py directly instead of as a package. +importlib.invalidate_caches() +import gguf # noqa: E402 + +importlib.reload(gguf) diff --git a/vllm/lib/python3.10/site-packages/gguf/gguf_reader.py b/vllm/lib/python3.10/site-packages/gguf/gguf_reader.py new file mode 100644 index 0000000000000000000000000000000000000000..e8e61abf86ae4a57a44b0e451fd14c9ee3619ae8 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/gguf_reader.py @@ -0,0 +1,317 @@ +# +# GGUF file reading/modification support. For API usage information, +# please see the files scripts/ for some fairly simple examples. +# +from __future__ import annotations + +import logging +import os +from collections import OrderedDict +from typing import Any, Literal, NamedTuple, TypeVar, Union + +import numpy as np +import numpy.typing as npt + +from .quants import quant_shape_to_byte_shape + +if __name__ == "__main__": + import sys + from pathlib import Path + + # Allow running file in package as a script. + sys.path.insert(0, str(Path(__file__).parent.parent)) + +from gguf.constants import ( + GGML_QUANT_SIZES, + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFValueType, +) + +logger = logging.getLogger(__name__) + +READER_SUPPORTED_VERSIONS = [2, GGUF_VERSION] + + +class ReaderField(NamedTuple): + # Offset to start of this field. + offset: int + + # Name of the field (not necessarily from file data). + name: str + + # Data parts. Some types have multiple components, such as strings + # that consist of a length followed by the string data. + parts: list[npt.NDArray[Any]] = [] + + # Indexes into parts that we can call the actual data. For example + # an array of strings will be populated with indexes to the actual + # string data. + data: list[int] = [-1] + + types: list[GGUFValueType] = [] + + +class ReaderTensor(NamedTuple): + name: str + tensor_type: GGMLQuantizationType + shape: npt.NDArray[np.uint32] + n_elements: int + n_bytes: int + data_offset: int + data: npt.NDArray[Any] + field: ReaderField + + +class GGUFReader: + # I - same as host, S - swapped + byte_order: Literal['I', 'S'] = 'I' + alignment: int = GGUF_DEFAULT_ALIGNMENT + data_offset: int + + # Note: Internal helper, API may change. + gguf_scalar_to_np: dict[GGUFValueType, type[np.generic]] = { + GGUFValueType.UINT8: np.uint8, + GGUFValueType.INT8: np.int8, + GGUFValueType.UINT16: np.uint16, + GGUFValueType.INT16: np.int16, + GGUFValueType.UINT32: np.uint32, + GGUFValueType.INT32: np.int32, + GGUFValueType.FLOAT32: np.float32, + GGUFValueType.UINT64: np.uint64, + GGUFValueType.INT64: np.int64, + GGUFValueType.FLOAT64: np.float64, + GGUFValueType.BOOL: np.bool_, + } + + def __init__(self, path: os.PathLike[str] | str, mode: Literal['r', 'r+', 'c'] = 'r'): + self.data = np.memmap(path, mode = mode) + offs = 0 + + # Check for GGUF magic + if self._get(offs, np.uint32, override_order = '<')[0] != GGUF_MAGIC: + raise ValueError('GGUF magic invalid') + offs += 4 + + # Check GGUF version + temp_version = self._get(offs, np.uint32) + if temp_version[0] & 65535 == 0: + # If we get 0 here that means it's (probably) a GGUF file created for + # the opposite byte order of the machine this script is running on. + self.byte_order = 'S' + temp_version = temp_version.newbyteorder(self.byte_order) + version = temp_version[0] + if version not in READER_SUPPORTED_VERSIONS: + raise ValueError(f'Sorry, file appears to be version {version} which we cannot handle') + self.fields: OrderedDict[str, ReaderField] = OrderedDict() + self.tensors: list[ReaderTensor] = [] + offs += self._push_field(ReaderField(offs, 'GGUF.version', [temp_version], [0], [GGUFValueType.UINT32])) + + # Check tensor count and kv count + temp_counts = self._get(offs, np.uint64, 2) + offs += self._push_field(ReaderField(offs, 'GGUF.tensor_count', [temp_counts[:1]], [0], [GGUFValueType.UINT64])) + offs += self._push_field(ReaderField(offs, 'GGUF.kv_count', [temp_counts[1:]], [0], [GGUFValueType.UINT64])) + tensor_count, kv_count = temp_counts + offs = self._build_fields(offs, kv_count) + + # Build Tensor Info Fields + offs, tensors_fields = self._build_tensor_info(offs, tensor_count) + new_align = self.fields.get('general.alignment') + if new_align is not None: + if new_align.types != [GGUFValueType.UINT32]: + raise ValueError('Bad type for general.alignment field') + self.alignment = new_align.parts[-1][0] + padding = offs % self.alignment + if padding != 0: + offs += self.alignment - padding + self.data_offset = offs + self._build_tensors(offs, tensors_fields) + + _DT = TypeVar('_DT', bound = npt.DTypeLike) + + # Fetch a key/value metadata field by key. + def get_field(self, key: str) -> Union[ReaderField, None]: + return self.fields.get(key, None) + + # Fetch a tensor from the list by index. + def get_tensor(self, idx: int) -> ReaderTensor: + return self.tensors[idx] + + def _get( + self, offset: int, dtype: npt.DTypeLike, count: int = 1, override_order: None | Literal['I', 'S', '<'] = None, + ) -> npt.NDArray[Any]: + count = int(count) + itemsize = int(np.empty([], dtype = dtype).itemsize) + end_offs = offset + itemsize * count + return ( + self.data[offset:end_offs] + .view(dtype = dtype)[:count] + .newbyteorder(override_order or self.byte_order) + ) + + def _push_field(self, field: ReaderField, skip_sum: bool = False) -> int: + if field.name in self.fields: + # TODO: add option to generate error on duplicate keys + # raise KeyError(f'Duplicate {field.name} already in list at offset {field.offset}') + + logger.warning(f'Duplicate key {field.name} at offset {field.offset}') + self.fields[field.name + '_{}'.format(field.offset)] = field + else: + self.fields[field.name] = field + return 0 if skip_sum else sum(int(part.nbytes) for part in field.parts) + + def _get_str(self, offset: int) -> tuple[npt.NDArray[np.uint64], npt.NDArray[np.uint8]]: + slen = self._get(offset, np.uint64) + return slen, self._get(offset + 8, np.uint8, slen[0]) + + def _get_field_parts( + self, orig_offs: int, raw_type: int, + ) -> tuple[int, list[npt.NDArray[Any]], list[int], list[GGUFValueType]]: + offs = orig_offs + types: list[GGUFValueType] = [] + gtype = GGUFValueType(raw_type) + types.append(gtype) + # Handle strings. + if gtype == GGUFValueType.STRING: + sparts: list[npt.NDArray[Any]] = list(self._get_str(offs)) + size = sum(int(part.nbytes) for part in sparts) + return size, sparts, [1], types + # Check if it's a simple scalar type. + nptype = self.gguf_scalar_to_np.get(gtype) + if nptype is not None: + val = self._get(offs, nptype) + return int(val.nbytes), [val], [0], types + # Handle arrays. + if gtype == GGUFValueType.ARRAY: + raw_itype = self._get(offs, np.uint32) + offs += int(raw_itype.nbytes) + alen = self._get(offs, np.uint64) + offs += int(alen.nbytes) + aparts: list[npt.NDArray[Any]] = [raw_itype, alen] + data_idxs: list[int] = [] + for idx in range(alen[0]): + curr_size, curr_parts, curr_idxs, curr_types = self._get_field_parts(offs, raw_itype[0]) + if idx == 0: + types += curr_types + idxs_offs = len(aparts) + aparts += curr_parts + data_idxs += (idx + idxs_offs for idx in curr_idxs) + offs += curr_size + return offs - orig_offs, aparts, data_idxs, types + # We can't deal with this one. + raise ValueError('Unknown/unhandled field type {gtype}') + + def _get_tensor_info_field(self, orig_offs: int) -> ReaderField: + offs = orig_offs + + # Get Tensor Name + name_len, name_data = self._get_str(offs) + offs += int(name_len.nbytes + name_data.nbytes) + + # Get Tensor Dimensions Count + n_dims = self._get(offs, np.uint32) + offs += int(n_dims.nbytes) + + # Get Tensor Dimension Array + dims = self._get(offs, np.uint64, n_dims[0]) + offs += int(dims.nbytes) + + # Get Tensor Encoding Scheme Type + raw_dtype = self._get(offs, np.uint32) + offs += int(raw_dtype.nbytes) + + # Get Tensor Offset + offset_tensor = self._get(offs, np.uint64) + offs += int(offset_tensor.nbytes) + + return ReaderField( + orig_offs, + str(bytes(name_data), encoding = 'utf-8'), + [name_len, name_data, n_dims, dims, raw_dtype, offset_tensor], + [1, 3, 4, 5], + ) + + def _build_fields(self, offs: int, count: int) -> int: + for _ in range(count): + orig_offs = offs + kv_klen, kv_kdata = self._get_str(offs) + offs += int(kv_klen.nbytes + kv_kdata.nbytes) + raw_kv_type = self._get(offs, np.uint32) + offs += int(raw_kv_type.nbytes) + parts: list[npt.NDArray[Any]] = [kv_klen, kv_kdata, raw_kv_type] + idxs_offs = len(parts) + field_size, field_parts, field_idxs, field_types = self._get_field_parts(offs, raw_kv_type[0]) + parts += field_parts + self._push_field(ReaderField( + orig_offs, + str(bytes(kv_kdata), encoding = 'utf-8'), + parts, + [idx + idxs_offs for idx in field_idxs], + field_types, + ), skip_sum = True) + offs += field_size + return offs + + def _build_tensor_info(self, offs: int, count: int) -> tuple[int, list[ReaderField]]: + tensor_fields = [] + for _ in range(count): + field = self._get_tensor_info_field(offs) + offs += sum(int(part.nbytes) for part in field.parts) + tensor_fields.append(field) + return offs, tensor_fields + + def _build_tensors(self, start_offs: int, fields: list[ReaderField]) -> None: + tensors = [] + tensor_names = set() # keep track of name to prevent duplicated tensors + for field in fields: + _name_len, name_data, _n_dims, dims, raw_dtype, offset_tensor = field.parts + # check if there's any tensor having same name already in the list + tensor_name = str(bytes(name_data), encoding = 'utf-8') + if tensor_name in tensor_names: + raise ValueError(f'Found duplicated tensor with name {tensor_name}') + tensor_names.add(tensor_name) + ggml_type = GGMLQuantizationType(raw_dtype[0]) + n_elems = int(np.prod(dims)) + np_dims = tuple(reversed(dims.tolist())) + block_size, type_size = GGML_QUANT_SIZES[ggml_type] + n_bytes = n_elems * type_size // block_size + data_offs = int(start_offs + offset_tensor[0]) + item_type: npt.DTypeLike + if ggml_type == GGMLQuantizationType.F16: + item_count = n_elems + item_type = np.float16 + elif ggml_type == GGMLQuantizationType.F32: + item_count = n_elems + item_type = np.float32 + elif ggml_type == GGMLQuantizationType.F64: + item_count = n_elems + item_type = np.float64 + elif ggml_type == GGMLQuantizationType.I8: + item_count = n_elems + item_type = np.int8 + elif ggml_type == GGMLQuantizationType.I16: + item_count = n_elems + item_type = np.int16 + elif ggml_type == GGMLQuantizationType.I32: + item_count = n_elems + item_type = np.int32 + elif ggml_type == GGMLQuantizationType.I64: + item_count = n_elems + item_type = np.int64 + else: + item_count = n_bytes + item_type = np.uint8 + np_dims = quant_shape_to_byte_shape(np_dims, ggml_type) + tensors.append(ReaderTensor( + name = tensor_name, + tensor_type = ggml_type, + shape = dims, + n_elements = n_elems, + n_bytes = n_bytes, + data_offset = data_offs, + data = self._get(data_offs, item_type, item_count).reshape(np_dims), + field = field, + )) + self.tensors = tensors diff --git a/vllm/lib/python3.10/site-packages/gguf/gguf_writer.py b/vllm/lib/python3.10/site-packages/gguf/gguf_writer.py new file mode 100644 index 0000000000000000000000000000000000000000..af3b98c679b0b66cd36d0a1ab5dafb8262936a0f --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/gguf_writer.py @@ -0,0 +1,888 @@ +from __future__ import annotations + +import logging +import os +import shutil +import struct +import tempfile +from dataclasses import dataclass +from enum import Enum, auto +from math import prod +from pathlib import Path +from io import BufferedWriter +from typing import IO, Any, Sequence, Mapping +from string import ascii_letters, digits + +import numpy as np + +from .constants import ( + GGUF_DEFAULT_ALIGNMENT, + GGUF_MAGIC, + GGUF_VERSION, + GGMLQuantizationType, + GGUFEndian, + GGUFValueType, + Keys, + RopeScalingType, + PoolingType, + TokenType, +) + +from .quants import quant_shape_from_byte_shape + +logger = logging.getLogger(__name__) + + +SHARD_NAME_FORMAT = "{:s}-{:05d}-of-{:05d}.gguf" + + +@dataclass +class TensorInfo: + shape: Sequence[int] + dtype: GGMLQuantizationType + nbytes: int + tensor: np.ndarray[Any, Any] | None = None + + +@dataclass +class GGUFValue: + value: Any + type: GGUFValueType + + +class WriterState(Enum): + NO_FILE = auto() + EMPTY = auto() + HEADER = auto() + KV_DATA = auto() + TI_DATA = auto() + WEIGHTS = auto() + + +class GGUFWriter: + fout: list[BufferedWriter] | None + path: Path | None + temp_file: tempfile.SpooledTemporaryFile[bytes] | None + tensors: list[dict[str, TensorInfo]] + kv_data: list[dict[str, GGUFValue]] + state: WriterState + _simple_value_packing = { + GGUFValueType.UINT8: "B", + GGUFValueType.INT8: "b", + GGUFValueType.UINT16: "H", + GGUFValueType.INT16: "h", + GGUFValueType.UINT32: "I", + GGUFValueType.INT32: "i", + GGUFValueType.FLOAT32: "f", + GGUFValueType.UINT64: "Q", + GGUFValueType.INT64: "q", + GGUFValueType.FLOAT64: "d", + GGUFValueType.BOOL: "?", + } + + def __init__( + self, path: os.PathLike[str] | str | None, arch: str, use_temp_file: bool = False, endianess: GGUFEndian = GGUFEndian.LITTLE, + split_max_tensors: int = 0, split_max_size: int = 0, dry_run: bool = False, small_first_shard: bool = False + ): + self.fout = None + self.path = Path(path) if path else None + self.arch = arch + self.endianess = endianess + self.data_alignment = GGUF_DEFAULT_ALIGNMENT + self.use_temp_file = use_temp_file + self.temp_file = None + self.tensors = [{}] + self.kv_data = [{}] + self.split_max_tensors = split_max_tensors + self.split_max_size = split_max_size + self.dry_run = dry_run + self.small_first_shard = small_first_shard + logger.info("gguf: This GGUF file is for {0} Endian only".format( + "Big" if self.endianess == GGUFEndian.BIG else "Little", + )) + self.state = WriterState.NO_FILE + + if self.small_first_shard: + self.tensors.append({}) + + self.add_architecture() + + def get_total_parameter_count(self) -> tuple[int, int, int, int]: + total_params = 0 + shared_params = 0 + expert_params = 0 + + expert_sum = 0 + n_expert_tensors = 0 + + last_lora_a: tuple[str, TensorInfo] | None = None + + for tensors in self.tensors: + for name, info in tensors.items(): + + shape = info.shape + + if name.endswith(".lora_a"): + last_lora_a = (name, info) + continue + elif name.endswith(".lora_b"): + if last_lora_a is None or last_lora_a[0] != name[:-1] + "a": + # Bail when the LoRA pair can't be found trivially + logger.warning("can't measure LoRA size correctly, tensor order is unusual") + return 0, 0, 0, 0 + else: + shape = (*shape[:-1], last_lora_a[1].shape[-1]) + + size = prod(shape) + + if "_exps." in name: + expert_params += (size // shape[-3]) + expert_sum += shape[-3] + n_expert_tensors += 1 + else: + shared_params += size + + total_params += size + + # Hopefully this should work even for variable-expert-count models + expert_count = (expert_sum // n_expert_tensors) if n_expert_tensors > 0 else 0 + + # Negate the total to signal it's likely not exact + if last_lora_a is not None: + total_params = -total_params + + # NOTE: keep the output in the same order as accepted by 'size_label' in gguf-py/gguf/utility.py + return total_params, shared_params, expert_params, expert_count + + def format_shard_names(self, path: Path) -> list[Path]: + if len(self.tensors) == 1: + return [path] + return [path.with_name(SHARD_NAME_FORMAT.format(path.stem, i + 1, len(self.tensors))) for i in range(len(self.tensors))] + + def open_output_file(self, path: Path | None = None) -> None: + if self.state is WriterState.EMPTY and self.fout is not None and (path is None or path == self.path): + # allow calling this multiple times as long as the path is the same + return + + if self.state is not WriterState.NO_FILE: + raise ValueError(f'Expected output file to be not yet opened, got {self.state}') + + if path is not None: + self.path = path + + if self.path is not None: + filenames = self.print_plan() + self.fout = [open(filename, "wb") for filename in filenames] + self.state = WriterState.EMPTY + + def print_plan(self) -> list[Path]: + logger.info("Writing the following files:") + assert self.path is not None + filenames = self.format_shard_names(self.path) + assert len(filenames) == len(self.tensors) + for name, tensors in zip(filenames, self.tensors): + logger.info(f"{name}: n_tensors = {len(tensors)}, total_size = {GGUFWriter.format_n_bytes_to_str(sum(ti.nbytes for ti in tensors.values()))}") + + if self.dry_run: + logger.info("Dry run, not writing files") + for name in filenames: + print(name) # noqa: NP100 + exit() + + return filenames + + def add_shard_kv_data(self) -> None: + if len(self.tensors) == 1: + return + + total_tensors = sum(len(t) for t in self.tensors) + assert self.fout is not None + total_splits = len(self.fout) + self.kv_data.extend({} for _ in range(len(self.kv_data), total_splits)) + for i, kv_data in enumerate(self.kv_data): + kv_data[Keys.Split.LLM_KV_SPLIT_NO] = GGUFValue(i, GGUFValueType.UINT16) + kv_data[Keys.Split.LLM_KV_SPLIT_COUNT] = GGUFValue(total_splits, GGUFValueType.UINT16) + kv_data[Keys.Split.LLM_KV_SPLIT_TENSORS_COUNT] = GGUFValue(total_tensors, GGUFValueType.INT32) + + def write_header_to_file(self, path: Path | None = None) -> None: + if len(self.tensors) == 1 and (self.split_max_tensors != 0 or self.split_max_size != 0): + logger.warning("Model fails split requirements, not splitting") + + self.open_output_file(path) + + if self.state is not WriterState.EMPTY: + raise ValueError(f'Expected output file to be empty, got {self.state}') + + assert self.fout is not None + assert len(self.fout) == len(self.tensors) + assert len(self.kv_data) == 1 + + self.add_shard_kv_data() + + for fout, tensors, kv_data in zip(self.fout, self.tensors, self.kv_data): + fout.write(self._pack(" None: + if self.state is not WriterState.HEADER: + raise ValueError(f'Expected output file to contain the header, got {self.state}') + assert self.fout is not None + + for fout, kv_data in zip(self.fout, self.kv_data): + kv_bytes = bytearray() + + for key, val in kv_data.items(): + kv_bytes += self._pack_val(key, GGUFValueType.STRING, add_vtype=False) + kv_bytes += self._pack_val(val.value, val.type, add_vtype=True) + + fout.write(kv_bytes) + + self.flush() + self.state = WriterState.KV_DATA + + def write_ti_data_to_file(self) -> None: + if self.state is not WriterState.KV_DATA: + raise ValueError(f'Expected output file to contain KV data, got {self.state}') + assert self.fout is not None + + for fout, tensors in zip(self.fout, self.tensors): + ti_data = bytearray() + offset_tensor = 0 + + for name, ti in tensors.items(): + ti_data += self._pack_val(name, GGUFValueType.STRING, add_vtype=False) + n_dims = len(ti.shape) + ti_data += self._pack("I", n_dims) + for j in range(n_dims): + ti_data += self._pack("Q", ti.shape[n_dims - 1 - j]) + ti_data += self._pack("I", ti.dtype) + ti_data += self._pack("Q", offset_tensor) + offset_tensor += GGUFWriter.ggml_pad(ti.nbytes, self.data_alignment) + + fout.write(ti_data) + fout.flush() + self.state = WriterState.TI_DATA + + def add_key_value(self, key: str, val: Any, vtype: GGUFValueType) -> None: + if any(key in kv_data for kv_data in self.kv_data): + raise ValueError(f'Duplicated key name {key!r}') + + self.kv_data[0][key] = GGUFValue(value=val, type=vtype) + + def add_uint8(self, key: str, val: int) -> None: + self.add_key_value(key,val, GGUFValueType.UINT8) + + def add_int8(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT8) + + def add_uint16(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.UINT16) + + def add_int16(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT16) + + def add_uint32(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.UINT32) + + def add_int32(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT32) + + def add_float32(self, key: str, val: float) -> None: + self.add_key_value(key, val, GGUFValueType.FLOAT32) + + def add_uint64(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.UINT64) + + def add_int64(self, key: str, val: int) -> None: + self.add_key_value(key, val, GGUFValueType.INT64) + + def add_float64(self, key: str, val: float) -> None: + self.add_key_value(key, val, GGUFValueType.FLOAT64) + + def add_bool(self, key: str, val: bool) -> None: + self.add_key_value(key, val, GGUFValueType.BOOL) + + def add_string(self, key: str, val: str) -> None: + if not val: + return + self.add_key_value(key, val, GGUFValueType.STRING) + + def add_array(self, key: str, val: Sequence[Any]) -> None: + if len(val) == 0: + return + self.add_key_value(key, val, GGUFValueType.ARRAY) + + @staticmethod + def ggml_pad(x: int, n: int) -> int: + return ((x + n - 1) // n) * n + + def add_tensor_info( + self, name: str, tensor_shape: Sequence[int], tensor_dtype: np.dtype, + tensor_nbytes: int, raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.state is not WriterState.NO_FILE: + raise ValueError(f'Expected output file to be not yet opened, got {self.state}') + + if any(name in tensors for tensors in self.tensors): + raise ValueError(f'Duplicated tensor name {name!r}') + + if raw_dtype is None: + if tensor_dtype == np.float16: + dtype = GGMLQuantizationType.F16 + elif tensor_dtype == np.float32: + dtype = GGMLQuantizationType.F32 + elif tensor_dtype == np.float64: + dtype = GGMLQuantizationType.F64 + elif tensor_dtype == np.int8: + dtype = GGMLQuantizationType.I8 + elif tensor_dtype == np.int16: + dtype = GGMLQuantizationType.I16 + elif tensor_dtype == np.int32: + dtype = GGMLQuantizationType.I32 + elif tensor_dtype == np.int64: + dtype = GGMLQuantizationType.I64 + else: + raise ValueError("Only F16, F32, F64, I8, I16, I32, I64 tensors are supported for now") + else: + dtype = raw_dtype + if tensor_dtype == np.uint8: + tensor_shape = quant_shape_from_byte_shape(tensor_shape, raw_dtype) + + # make sure there is at least one tensor before splitting + if len(self.tensors[-1]) > 0: + if ( # split when over tensor limit + self.split_max_tensors != 0 + and len(self.tensors[-1]) >= self.split_max_tensors + ) or ( # split when over size limit + self.split_max_size != 0 + and sum(ti.nbytes for ti in self.tensors[-1].values()) + tensor_nbytes > self.split_max_size + ): + self.tensors.append({}) + + self.tensors[-1][name] = TensorInfo(shape=tensor_shape, dtype=dtype, nbytes=tensor_nbytes) + + def add_tensor( + self, name: str, tensor: np.ndarray[Any, Any], raw_shape: Sequence[int] | None = None, + raw_dtype: GGMLQuantizationType | None = None, + ) -> None: + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + if self.use_temp_file and self.temp_file is None: + fp = tempfile.SpooledTemporaryFile(mode="w+b", max_size=256 * 1024 * 1024) + fp.seek(0) + self.temp_file = fp + + shape: Sequence[int] = raw_shape if raw_shape is not None else tensor.shape + self.add_tensor_info(name, shape, tensor.dtype, tensor.nbytes, raw_dtype=raw_dtype) + + if self.temp_file is None: + self.tensors[-1][name].tensor = tensor + return + + tensor.tofile(self.temp_file) + self.write_padding(self.temp_file, tensor.nbytes) + + def write_padding(self, fp: IO[bytes], n: int, align: int | None = None) -> None: + pad = GGUFWriter.ggml_pad(n, align if align is not None else self.data_alignment) - n + if pad != 0: + fp.write(bytes([0] * pad)) + + def write_tensor_data(self, tensor: np.ndarray[Any, Any]) -> None: + if self.state is not WriterState.TI_DATA and self.state is not WriterState.WEIGHTS: + raise ValueError(f'Expected output file to contain tensor info or weights, got {self.state}') + assert self.fout is not None + + if self.endianess == GGUFEndian.BIG: + tensor.byteswap(inplace=True) + + file_id = -1 + for i, tensors in enumerate(self.tensors): + if len(tensors) > 0: + file_id = i + break + + fout = self.fout[file_id] + + # pop the first tensor info + # TODO: cleaner way to get the first key + first_tensor_name = [name for name, _ in zip(self.tensors[file_id].keys(), range(1))][0] + ti = self.tensors[file_id].pop(first_tensor_name) + assert ti.nbytes == tensor.nbytes + + self.write_padding(fout, fout.tell()) + tensor.tofile(fout) + self.write_padding(fout, tensor.nbytes) + + self.state = WriterState.WEIGHTS + + def write_tensors_to_file(self, *, progress: bool = False) -> None: + self.write_ti_data_to_file() + + assert self.fout is not None + + for fout in self.fout: + self.write_padding(fout, fout.tell()) + + if self.temp_file is None: + shard_bar = None + bar = None + + if progress: + from tqdm import tqdm + + total_bytes = sum(ti.nbytes for t in self.tensors for ti in t.values()) + + if len(self.fout) > 1: + shard_bar = tqdm(desc=f"Shard (0/{len(self.fout)})", total=None, unit="byte", unit_scale=True) + bar = tqdm(desc="Writing", total=total_bytes, unit="byte", unit_scale=True) + + for i, (fout, tensors) in enumerate(zip(self.fout, self.tensors)): + if shard_bar is not None: + shard_bar.set_description(f"Shard ({i + 1}/{len(self.fout)})") + total = sum(ti.nbytes for ti in tensors.values()) + shard_bar.reset(total=(total if total > 0 else None)) + + # relying on the fact that Python dicts preserve insertion order (since 3.7) + for ti in tensors.values(): + assert ti.tensor is not None # can only iterate once over the tensors + assert ti.tensor.nbytes == ti.nbytes + ti.tensor.tofile(fout) + if shard_bar is not None: + shard_bar.update(ti.nbytes) + if bar is not None: + bar.update(ti.nbytes) + self.write_padding(fout, ti.nbytes) + ti.tensor = None + else: + self.temp_file.seek(0) + + shutil.copyfileobj(self.temp_file, self.fout[0 if not self.small_first_shard else 1]) + self.flush() + self.temp_file.close() + + self.state = WriterState.WEIGHTS + + def flush(self) -> None: + assert self.fout is not None + for fout in self.fout: + fout.flush() + + def close(self) -> None: + if self.fout is not None: + for fout in self.fout: + fout.close() + self.fout = None + + def add_type(self, type_name: str) -> None: + self.add_string(Keys.General.TYPE, type_name) + + def add_architecture(self) -> None: + self.add_string(Keys.General.ARCHITECTURE, self.arch) + + def add_quantization_version(self, quantization_version: int) -> None: + self.add_uint32(Keys.General.QUANTIZATION_VERSION, quantization_version) + + def add_custom_alignment(self, alignment: int) -> None: + self.data_alignment = alignment + self.add_uint32(Keys.General.ALIGNMENT, alignment) + + def add_file_type(self, ftype: int) -> None: + self.add_uint32(Keys.General.FILE_TYPE, ftype) + + def add_name(self, name: str) -> None: + self.add_string(Keys.General.NAME, name) + + def add_author(self, author: str) -> None: + self.add_string(Keys.General.AUTHOR, author) + + def add_version(self, version: str) -> None: + self.add_string(Keys.General.VERSION, version) + + def add_organization(self, organization: str) -> None: + self.add_string(Keys.General.ORGANIZATION, organization) + + def add_finetune(self, finetune: str) -> None: + self.add_string(Keys.General.FINETUNE, finetune) + + def add_basename(self, basename: str) -> None: + self.add_string(Keys.General.BASENAME, basename) + + def add_description(self, description: str) -> None: + self.add_string(Keys.General.DESCRIPTION, description) + + def add_quantized_by(self, quantized: str) -> None: + self.add_string(Keys.General.QUANTIZED_BY, quantized) + + def add_size_label(self, size_label: str) -> None: + self.add_string(Keys.General.SIZE_LABEL, size_label) + + def add_license(self, license: str) -> None: + self.add_string(Keys.General.LICENSE, license) + + def add_license_name(self, license: str) -> None: + self.add_string(Keys.General.LICENSE_NAME, license) + + def add_license_link(self, license: str) -> None: + self.add_string(Keys.General.LICENSE_LINK, license) + + def add_url(self, url: str) -> None: + self.add_string(Keys.General.URL, url) + + def add_doi(self, doi: str) -> None: + self.add_string(Keys.General.DOI, doi) + + def add_uuid(self, uuid: str) -> None: + self.add_string(Keys.General.UUID, uuid) + + def add_repo_url(self, repo_url: str) -> None: + self.add_string(Keys.General.REPO_URL, repo_url) + + def add_source_url(self, url: str) -> None: + self.add_string(Keys.General.SOURCE_URL, url) + + def add_source_doi(self, doi: str) -> None: + self.add_string(Keys.General.SOURCE_DOI, doi) + + def add_source_uuid(self, uuid: str) -> None: + self.add_string(Keys.General.SOURCE_UUID, uuid) + + def add_source_repo_url(self, repo_url: str) -> None: + self.add_string(Keys.General.SOURCE_REPO_URL, repo_url) + + def add_base_model_count(self, source_count: int) -> None: + self.add_uint32(Keys.General.BASE_MODEL_COUNT, source_count) + + def add_base_model_name(self, source_id: int, name: str) -> None: + self.add_string(Keys.General.BASE_MODEL_NAME.format(id=source_id), name) + + def add_base_model_author(self, source_id: int, author: str) -> None: + self.add_string(Keys.General.BASE_MODEL_AUTHOR.format(id=source_id), author) + + def add_base_model_version(self, source_id: int, version: str) -> None: + self.add_string(Keys.General.BASE_MODEL_VERSION.format(id=source_id), version) + + def add_base_model_organization(self, source_id: int, organization: str) -> None: + self.add_string(Keys.General.BASE_MODEL_ORGANIZATION.format(id=source_id), organization) + + def add_base_model_url(self, source_id: int, url: str) -> None: + self.add_string(Keys.General.BASE_MODEL_URL.format(id=source_id), url) + + def add_base_model_doi(self, source_id: int, doi: str) -> None: + self.add_string(Keys.General.BASE_MODEL_DOI.format(id=source_id), doi) + + def add_base_model_uuid(self, source_id: int, uuid: str) -> None: + self.add_string(Keys.General.BASE_MODEL_UUID.format(id=source_id), uuid) + + def add_base_model_repo_url(self, source_id: int, repo_url: str) -> None: + self.add_string(Keys.General.BASE_MODEL_REPO_URL.format(id=source_id), repo_url) + + def add_tags(self, tags: Sequence[str]) -> None: + self.add_array(Keys.General.TAGS, tags) + + def add_languages(self, languages: Sequence[str]) -> None: + self.add_array(Keys.General.LANGUAGES, languages) + + def add_datasets(self, datasets: Sequence[str]) -> None: + self.add_array(Keys.General.DATASETS, datasets) + + def add_tensor_data_layout(self, layout: str) -> None: + self.add_string(Keys.LLM.TENSOR_DATA_LAYOUT.format(arch=self.arch), layout) + + def add_vocab_size(self, size: int) -> None: + self.add_uint32(Keys.LLM.VOCAB_SIZE.format(arch=self.arch), size) + + def add_context_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.CONTEXT_LENGTH.format(arch=self.arch), length) + + def add_embedding_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length) + + def add_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length) + + def add_leading_dense_block_count(self, length: int) -> None: + self.add_uint32(Keys.LLM.LEADING_DENSE_BLOCK_COUNT.format(arch=self.arch), length) + + def add_feed_forward_length(self, length: int | Sequence[int]) -> None: + if isinstance(length, int): + self.add_uint32(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + else: + self.add_array(Keys.LLM.FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_expert_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_expert_shared_feed_forward_length(self, length: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_FEED_FORWARD_LENGTH.format(arch=self.arch), length) + + def add_parallel_residual(self, use: bool) -> None: + self.add_bool(Keys.LLM.USE_PARALLEL_RESIDUAL.format(arch=self.arch), use) + + def add_decoder_start_token_id(self, id: int) -> None: + self.add_uint32(Keys.LLM.DECODER_START_TOKEN_ID.format(arch=self.arch), id) + + def add_head_count(self, count: int | Sequence[int]) -> None: + if isinstance(count, int): + self.add_uint32(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + else: + self.add_array(Keys.Attention.HEAD_COUNT.format(arch=self.arch), count) + + def add_head_count_kv(self, count: int | Sequence[int]) -> None: + if isinstance(count, int): + self.add_uint32(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + else: + self.add_array(Keys.Attention.HEAD_COUNT_KV.format(arch=self.arch), count) + + def add_key_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.KEY_LENGTH.format(arch=self.arch), length) + + def add_value_length(self, length: int) -> None: + self.add_uint32(Keys.Attention.VALUE_LENGTH.format(arch=self.arch), length) + + def add_max_alibi_bias(self, bias: float) -> None: + self.add_float32(Keys.Attention.MAX_ALIBI_BIAS.format(arch=self.arch), bias) + + def add_clamp_kqv(self, value: float) -> None: + self.add_float32(Keys.Attention.CLAMP_KQV.format(arch=self.arch), value) + + def add_logit_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.LOGIT_SCALE.format(arch=self.arch), value) + + def add_attn_logit_softcapping(self, value: float) -> None: + self.add_float32(Keys.LLM.ATTN_LOGIT_SOFTCAPPING.format(arch=self.arch), value) + + def add_final_logit_softcapping(self, value: float) -> None: + self.add_float32(Keys.LLM.FINAL_LOGIT_SOFTCAPPING.format(arch=self.arch), value) + + def add_expert_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_COUNT.format(arch=self.arch), count) + + def add_expert_used_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_USED_COUNT.format(arch=self.arch), count) + + def add_expert_shared_count(self, count: int) -> None: + self.add_uint32(Keys.LLM.EXPERT_SHARED_COUNT.format(arch=self.arch), count) + + def add_expert_weights_scale(self, value: float) -> None: + self.add_float32(Keys.LLM.EXPERT_WEIGHTS_SCALE.format(arch=self.arch), value) + + def add_layer_norm_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_EPS.format(arch=self.arch), value) + + def add_layer_norm_rms_eps(self, value: float) -> None: + self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value) + + def add_causal_attention(self, value: bool) -> None: + self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value) + + def add_q_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.Q_LORA_RANK.format(arch=self.arch), length) + + def add_kv_lora_rank(self, length: int) -> None: + self.add_uint32(Keys.Attention.KV_LORA_RANK.format(arch=self.arch), length) + + def add_relative_attn_buckets_count(self, value: int) -> None: + self.add_uint32(Keys.Attention.REL_BUCKETS_COUNT.format(arch=self.arch), value) + + def add_sliding_window(self, value: int) -> None: + self.add_uint32(Keys.Attention.SLIDING_WINDOW.format(arch=self.arch), value) + + def add_pooling_type(self, value: PoolingType) -> None: + self.add_uint32(Keys.LLM.POOLING_TYPE.format(arch=self.arch), value.value) + + def add_rope_dimension_count(self, count: int) -> None: + self.add_uint32(Keys.Rope.DIMENSION_COUNT.format(arch=self.arch), count) + + def add_rope_freq_base(self, value: float) -> None: + self.add_float32(Keys.Rope.FREQ_BASE.format(arch=self.arch), value) + + def add_rope_scaling_type(self, value: RopeScalingType) -> None: + self.add_string(Keys.Rope.SCALING_TYPE.format(arch=self.arch), value.value) + + def add_rope_scaling_factor(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_attn_factors(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_ATTN_FACTOR.format(arch=self.arch), value) + + def add_rope_scaling_orig_ctx_len(self, value: int) -> None: + self.add_uint32(Keys.Rope.SCALING_ORIG_CTX_LEN.format(arch=self.arch), value) + + def add_rope_scaling_finetuned(self, value: bool) -> None: + self.add_bool(Keys.Rope.SCALING_FINETUNED.format(arch=self.arch), value) + + def add_rope_scaling_yarn_log_mul(self, value: float) -> None: + self.add_float32(Keys.Rope.SCALING_YARN_LOG_MUL.format(arch=self.arch), value) + + def add_ssm_conv_kernel(self, value: int) -> None: + self.add_uint32(Keys.SSM.CONV_KERNEL.format(arch=self.arch), value) + + def add_ssm_inner_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.INNER_SIZE.format(arch=self.arch), value) + + def add_ssm_state_size(self, value: int) -> None: + self.add_uint32(Keys.SSM.STATE_SIZE.format(arch=self.arch), value) + + def add_ssm_time_step_rank(self, value: int) -> None: + self.add_uint32(Keys.SSM.TIME_STEP_RANK.format(arch=self.arch), value) + + def add_ssm_dt_b_c_rms(self, value: bool) -> None: + self.add_bool(Keys.SSM.DT_B_C_RMS.format(arch=self.arch), value) + + def add_tokenizer_model(self, model: str) -> None: + self.add_string(Keys.Tokenizer.MODEL, model) + + def add_tokenizer_pre(self, pre: str) -> None: + self.add_string(Keys.Tokenizer.PRE, pre) + + def add_token_list(self, tokens: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.LIST, tokens) + + def add_token_merges(self, merges: Sequence[str] | Sequence[bytes] | Sequence[bytearray]) -> None: + self.add_array(Keys.Tokenizer.MERGES, merges) + + def add_token_types(self, types: Sequence[TokenType] | Sequence[int]) -> None: + self.add_array(Keys.Tokenizer.TOKEN_TYPE, types) + + def add_token_type_count(self, value: int) -> None: + self.add_uint32(Keys.Tokenizer.TOKEN_TYPE_COUNT, value) + + def add_token_scores(self, scores: Sequence[float]) -> None: + self.add_array(Keys.Tokenizer.SCORES, scores) + + def add_bos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.BOS_ID, id) + + def add_eos_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOS_ID, id) + + def add_unk_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.UNK_ID, id) + + def add_sep_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SEP_ID, id) + + def add_pad_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PAD_ID, id) + + def add_cls_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.CLS_ID, id) + + def add_mask_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.MASK_ID, id) + + def add_add_bos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_BOS, value) + + def add_add_eos_token(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_EOS, value) + + def add_add_space_prefix(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.ADD_PREFIX, value) + + def add_remove_extra_whitespaces(self, value: bool) -> None: + self.add_bool(Keys.Tokenizer.REMOVE_EXTRA_WS, value) + + def add_precompiled_charsmap(self, charsmap: Sequence[bytes]) -> None: + self.add_array(Keys.Tokenizer.PRECOMPILED_CHARSMAP, charsmap) + + def add_chat_template(self, value: str | Sequence[Mapping[str, str]]) -> None: + if not isinstance(value, str): + template_default = None + template_names = set() + + for choice in value: + name = choice.get('name', '') + template = choice.get('template') + + # Allowing non-alphanumerical characters in template name is probably not a good idea, so filter it + name = ''.join((c if c in ascii_letters + digits else '_' for c in name)) + + if name and template is not None: + if name == 'default': + template_default = template + else: + template_names.add(name) + self.add_string(Keys.Tokenizer.CHAT_TEMPLATE_N.format(name=name), template) + + if template_names: + self.add_array(Keys.Tokenizer.CHAT_TEMPLATES, list(template_names)) + + if template_default is None: + return + + value = template_default + + self.add_string(Keys.Tokenizer.CHAT_TEMPLATE, value) + + def add_prefix_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.PREFIX_ID, id) + + def add_suffix_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.SUFFIX_ID, id) + + def add_middle_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.MIDDLE_ID, id) + + def add_eot_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOT_ID, id) + + def add_eom_token_id(self, id: int) -> None: + self.add_uint32(Keys.Tokenizer.EOM_ID, id) + + def _pack(self, fmt: str, value: Any, skip_pack_prefix: bool = False) -> bytes: + pack_prefix = '' + if not skip_pack_prefix: + pack_prefix = '<' if self.endianess == GGUFEndian.LITTLE else '>' + return struct.pack(f'{pack_prefix}{fmt}', value) + + def _pack_val(self, val: Any, vtype: GGUFValueType, add_vtype: bool) -> bytes: + kv_data = bytearray() + + if add_vtype: + kv_data += self._pack("I", vtype) + + pack_fmt = self._simple_value_packing.get(vtype) + if pack_fmt is not None: + kv_data += self._pack(pack_fmt, val, skip_pack_prefix = vtype == GGUFValueType.BOOL) + elif vtype == GGUFValueType.STRING: + encoded_val = val.encode("utf-8") if isinstance(val, str) else val + kv_data += self._pack("Q", len(encoded_val)) + kv_data += encoded_val + elif vtype == GGUFValueType.ARRAY: + + if not isinstance(val, Sequence): + raise ValueError("Invalid GGUF metadata array, expecting sequence") + + if len(val) == 0: + raise ValueError("Invalid GGUF metadata array. Empty array") + + if isinstance(val, bytes): + ltype = GGUFValueType.UINT8 + else: + ltype = GGUFValueType.get_type(val[0]) + if not all(GGUFValueType.get_type(i) is ltype for i in val[1:]): + raise ValueError("All items in a GGUF array should be of the same type") + kv_data += self._pack("I", ltype) + kv_data += self._pack("Q", len(val)) + for item in val: + kv_data += self._pack_val(item, ltype, add_vtype=False) + else: + raise ValueError("Invalid GGUF metadata value type or value") + + return kv_data + + @staticmethod + def format_n_bytes_to_str(num: int) -> str: + if num == 0: + return "negligible - metadata only" + fnum = float(num) + for unit in ("", "K", "M", "G"): + if abs(fnum) < 1000.0: + return f"{fnum:3.1f}{unit}" + fnum /= 1000.0 + return f"{fnum:.1f}T - over 1TB, split recommended" diff --git a/vllm/lib/python3.10/site-packages/gguf/lazy.py b/vllm/lib/python3.10/site-packages/gguf/lazy.py new file mode 100644 index 0000000000000000000000000000000000000000..8d4fece2dca86983286a3c0de15ca86578ce4dfa --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/lazy.py @@ -0,0 +1,213 @@ +from __future__ import annotations +from abc import ABC, ABCMeta, abstractmethod + +import logging +from typing import Any, Callable + +import numpy as np +from numpy.typing import DTypeLike + + +logger = logging.getLogger(__name__) + + +class LazyMeta(ABCMeta): + + def __new__(cls, name: str, bases: tuple[type, ...], namespace: dict[str, Any], **kwargs): + def __getattr__(self, name: str) -> Any: + meta_attr = getattr(self._meta, name) + if callable(meta_attr): + return type(self)._wrap_fn( + (lambda s, *args, **kwargs: getattr(s, name)(*args, **kwargs)), + use_self=self, + ) + elif isinstance(meta_attr, self._tensor_type): + # e.g. self.T with torch.Tensor should still be wrapped + return type(self)._wrap_fn(lambda s: getattr(s, name))(self) + else: + # no need to wrap non-tensor properties, + # and they likely don't depend on the actual contents of the tensor + return meta_attr + + namespace["__getattr__"] = __getattr__ + + # need to make a builder for the wrapped wrapper to copy the name, + # or else it fails with very cryptic error messages, + # because somehow the same string would end up in every closures + def mk_wrap(op_name: str, *, meta_noop: bool = False): + # need to wrap the wrapper to get self + def wrapped_special_op(self, *args, **kwargs): + return type(self)._wrap_fn( + getattr(type(self)._tensor_type, op_name), + meta_noop=meta_noop, + )(self, *args, **kwargs) + return wrapped_special_op + + # special methods bypass __getattr__, so they need to be added manually + # ref: https://docs.python.org/3/reference/datamodel.html#special-lookup + # NOTE: doing this from a metaclass is very convenient + # TODO: make this even more comprehensive + for binary_op in ( + "lt", "le", "eq", "ne", "ge", "gt", "not" + "abs", "add", "and", "floordiv", "invert", "lshift", "mod", "mul", "matmul", + "neg", "or", "pos", "pow", "rshift", "sub", "truediv", "xor", + "iadd", "iand", "ifloordiv", "ilshift", "imod", "imul", "ior", "irshift", "isub", "ixor", + "radd", "rand", "rfloordiv", "rmul", "ror", "rpow", "rsub", "rtruediv", "rxor", + ): + attr_name = f"__{binary_op}__" + # the result of these operators usually has the same shape and dtype as the input, + # so evaluation on the meta tensor can be skipped. + namespace[attr_name] = mk_wrap(attr_name, meta_noop=True) + + for special_op in ( + "getitem", "setitem", "len", + ): + attr_name = f"__{special_op}__" + namespace[attr_name] = mk_wrap(attr_name, meta_noop=False) + + return super().__new__(cls, name, bases, namespace, **kwargs) + + +# Tree of lazy tensors +class LazyBase(ABC, metaclass=LazyMeta): + _tensor_type: type + _meta: Any + _data: Any | None + _args: tuple + _kwargs: dict[str, Any] + _func: Callable[[Any], Any] | None + + def __init__(self, *, meta: Any, data: Any | None = None, args: tuple = (), kwargs: dict[str, Any] | None = None, func: Callable[[Any], Any] | None = None): + super().__init__() + self._meta = meta + self._data = data + self._args = args + self._kwargs = kwargs if kwargs is not None else {} + self._func = func + assert self._func is not None or self._data is not None + + def __init_subclass__(cls) -> None: + if "_tensor_type" not in cls.__dict__: + raise TypeError(f"property '_tensor_type' must be defined for {cls!r}") + return super().__init_subclass__() + + @staticmethod + def _recurse_apply(o: Any, fn: Callable[[Any], Any]) -> Any: + # TODO: dict and set + if isinstance(o, (list, tuple)): + L = [] + for item in o: + L.append(LazyBase._recurse_apply(item, fn)) + if isinstance(o, tuple): + L = tuple(L) + return L + elif isinstance(o, LazyBase): + return fn(o) + else: + return o + + @classmethod + def _wrap_fn(cls, fn: Callable, *, use_self: LazyBase | None = None, meta_noop: bool | DTypeLike | tuple[DTypeLike, Callable[[tuple[int, ...]], tuple[int, ...]]] = False) -> Callable[[Any], Any]: + def wrapped_fn(*args, **kwargs): + if kwargs is None: + kwargs = {} + args = ((use_self,) if use_self is not None else ()) + args + + meta_args = LazyBase._recurse_apply(args, lambda t: t._meta) + # TODO: maybe handle tensors in kwargs too + + if isinstance(meta_noop, bool) and not meta_noop: + try: + res = fn(*meta_args, **kwargs) + except NotImplementedError: + # running some operations on PyTorch's Meta tensors can cause this exception + res = None + else: + # some operators don't need to actually run on the meta tensors + assert len(args) > 0 + res = args[0] + assert isinstance(res, cls) + res = res._meta + # allow operations to override the dtype and shape + if meta_noop is not True: + if isinstance(meta_noop, tuple): + dtype, shape = meta_noop + assert callable(shape) + res = cls.meta_with_dtype_and_shape(dtype, shape(res.shape)) + else: + res = cls.meta_with_dtype_and_shape(meta_noop, res.shape) + + if isinstance(res, cls._tensor_type): + return cls(meta=cls.eager_to_meta(res), args=args, kwargs=kwargs, func=fn) + else: + del res # not needed + # non-tensor return likely relies on the contents of the args + # (e.g. the result of torch.equal) + eager_args = cls.to_eager(args) + return fn(*eager_args, **kwargs) + return wrapped_fn + + @classmethod + def to_eager(cls, t: Any) -> Any: + def simple_to_eager(_t: LazyBase) -> Any: + if _t._data is not None: + return _t._data + + # NOTE: there's a recursion limit in Python (usually 1000) + + assert _t._func is not None + _t._args = cls._recurse_apply(_t._args, simple_to_eager) + _t._data = _t._func(*_t._args, **_t._kwargs) + # sanity check + assert _t._data is not None + assert _t._data.dtype == _t._meta.dtype + assert _t._data.shape == _t._meta.shape + + return _t._data + + # recurse into lists and/or tuples, keeping their structure + return cls._recurse_apply(t, simple_to_eager) + + @classmethod + def eager_to_meta(cls, t: Any) -> Any: + return cls.meta_with_dtype_and_shape(t.dtype, t.shape) + + # must be overridden, meta tensor init is backend-specific + @classmethod + @abstractmethod + def meta_with_dtype_and_shape(cls, dtype: Any, shape: Any) -> Any: pass + + @classmethod + def from_eager(cls, t: Any) -> Any: + if type(t) is cls: + # already lazy + return t + elif isinstance(t, cls._tensor_type): + return cls(meta=cls.eager_to_meta(t), data=t) + else: + return TypeError(f"{type(t)!r} is not compatible with {cls._tensor_type!r}") + + +class LazyNumpyTensor(LazyBase): + _tensor_type = np.ndarray + + shape: tuple[int, ...] # Makes the type checker happy in quants.py + + @classmethod + def meta_with_dtype_and_shape(cls, dtype: DTypeLike, shape: tuple[int, ...]) -> np.ndarray[Any, Any]: + # The initial idea was to use np.nan as the fill value, + # but non-float types like np.int16 can't use that. + # So zero it is. + cheat = np.zeros(1, dtype) + return np.lib.stride_tricks.as_strided(cheat, shape, (0 for _ in shape)) + + def astype(self, dtype, *args, **kwargs): + meta = type(self).meta_with_dtype_and_shape(dtype, self._meta.shape) + full_args = (self, dtype,) + args + return type(self)(meta=meta, args=full_args, kwargs=kwargs, func=(lambda a, *args, **kwargs: a.astype(*args, **kwargs))) + + def tofile(self, *args, **kwargs): + eager = LazyNumpyTensor.to_eager(self) + return eager.tofile(*args, **kwargs) + + # TODO: __array_function__ diff --git a/vllm/lib/python3.10/site-packages/gguf/metadata.py b/vllm/lib/python3.10/site-packages/gguf/metadata.py new file mode 100644 index 0000000000000000000000000000000000000000..db318542a279b606e95ff51c82fd77615fce30b8 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/metadata.py @@ -0,0 +1,510 @@ +from __future__ import annotations + +import re +import json +import yaml +import logging +from pathlib import Path +from typing import Any, Literal, Optional +from dataclasses import dataclass + +from .constants import Keys + +import gguf + +logger = logging.getLogger("metadata") + + +@dataclass +class Metadata: + # Authorship Metadata to be written to GGUF KV Store + name: Optional[str] = None + author: Optional[str] = None + version: Optional[str] = None + organization: Optional[str] = None + finetune: Optional[str] = None + basename: Optional[str] = None + description: Optional[str] = None + quantized_by: Optional[str] = None + size_label: Optional[str] = None + url: Optional[str] = None + doi: Optional[str] = None + uuid: Optional[str] = None + repo_url: Optional[str] = None + source_url: Optional[str] = None + source_doi: Optional[str] = None + source_uuid: Optional[str] = None + source_repo_url: Optional[str] = None + license: Optional[str] = None + license_name: Optional[str] = None + license_link: Optional[str] = None + base_models: Optional[list[dict]] = None + tags: Optional[list[str]] = None + languages: Optional[list[str]] = None + datasets: Optional[list[str]] = None + + @staticmethod + def load(metadata_override_path: Optional[Path] = None, model_path: Optional[Path] = None, model_name: Optional[str] = None, total_params: int = 0) -> Metadata: + # This grabs as many contextual authorship metadata as possible from the model repository + # making any conversion as required to match the gguf kv store metadata format + # as well as giving users the ability to override any authorship metadata that may be incorrect + + # Create a new Metadata instance + metadata = Metadata() + + model_card = Metadata.load_model_card(model_path) + hf_params = Metadata.load_hf_parameters(model_path) + # TODO: load adapter_config.json when possible, it usually contains the base model of the LoRA adapter + + # heuristics + metadata = Metadata.apply_metadata_heuristic(metadata, model_card, hf_params, model_path, total_params) + + # Metadata Override File Provided + # This is based on LLM_KV_NAMES mapping in llama.cpp + metadata_override = Metadata.load_metadata_override(metadata_override_path) + + metadata.name = metadata_override.get(Keys.General.NAME, metadata.name) + metadata.author = metadata_override.get(Keys.General.AUTHOR, metadata.author) + metadata.version = metadata_override.get(Keys.General.VERSION, metadata.version) + metadata.organization = metadata_override.get(Keys.General.ORGANIZATION, metadata.organization) + + metadata.finetune = metadata_override.get(Keys.General.FINETUNE, metadata.finetune) + metadata.basename = metadata_override.get(Keys.General.BASENAME, metadata.basename) + + metadata.description = metadata_override.get(Keys.General.DESCRIPTION, metadata.description) + metadata.quantized_by = metadata_override.get(Keys.General.QUANTIZED_BY, metadata.quantized_by) + + metadata.size_label = metadata_override.get(Keys.General.SIZE_LABEL, metadata.size_label) + metadata.license_name = metadata_override.get(Keys.General.LICENSE_NAME, metadata.license_name) + metadata.license_link = metadata_override.get(Keys.General.LICENSE_LINK, metadata.license_link) + + metadata.url = metadata_override.get(Keys.General.URL, metadata.url) + metadata.doi = metadata_override.get(Keys.General.DOI, metadata.doi) + metadata.uuid = metadata_override.get(Keys.General.UUID, metadata.uuid) + metadata.repo_url = metadata_override.get(Keys.General.REPO_URL, metadata.repo_url) + + metadata.source_url = metadata_override.get(Keys.General.SOURCE_URL, metadata.source_url) + metadata.source_doi = metadata_override.get(Keys.General.SOURCE_DOI, metadata.source_doi) + metadata.source_uuid = metadata_override.get(Keys.General.SOURCE_UUID, metadata.source_uuid) + metadata.source_repo_url = metadata_override.get(Keys.General.SOURCE_REPO_URL, metadata.source_repo_url) + + # Base Models is received here as an array of models + metadata.base_models = metadata_override.get("general.base_models", metadata.base_models) + + metadata.tags = metadata_override.get(Keys.General.TAGS, metadata.tags) + metadata.languages = metadata_override.get(Keys.General.LANGUAGES, metadata.languages) + metadata.datasets = metadata_override.get(Keys.General.DATASETS, metadata.datasets) + + # Direct Metadata Override (via direct cli argument) + if model_name is not None: + metadata.name = model_name + + return metadata + + @staticmethod + def load_metadata_override(metadata_override_path: Optional[Path] = None) -> dict[str, Any]: + if metadata_override_path is None or not metadata_override_path.is_file(): + return {} + + with open(metadata_override_path, "r", encoding="utf-8") as f: + return json.load(f) + + @staticmethod + def load_model_card(model_path: Optional[Path] = None) -> dict[str, Any]: + if model_path is None or not model_path.is_dir(): + return {} + + model_card_path = model_path / "README.md" + + if not model_card_path.is_file(): + return {} + + # The model card metadata is assumed to always be in YAML + # ref: https://github.com/huggingface/transformers/blob/a5c642fe7a1f25d3bdcd76991443ba6ff7ee34b2/src/transformers/modelcard.py#L468-L473 + with open(model_card_path, "r", encoding="utf-8") as f: + if f.readline() == "---\n": + raw = f.read().partition("---\n")[0] + data = yaml.safe_load(raw) + if isinstance(data, dict): + return data + else: + logger.error(f"while reading YAML model card frontmatter, data is {type(data)} instead of dict") + return {} + else: + return {} + + @staticmethod + def load_hf_parameters(model_path: Optional[Path] = None) -> dict[str, Any]: + if model_path is None or not model_path.is_dir(): + return {} + + config_path = model_path / "config.json" + + if not config_path.is_file(): + return {} + + with open(config_path, "r", encoding="utf-8") as f: + return json.load(f) + + @staticmethod + def id_to_title(string): + # Convert capitalization into title form unless acronym or version number + return ' '.join([w.title() if w.islower() and not re.match(r'^(v\d+(?:\.\d+)*|\d.*)$', w) else w for w in string.strip().replace('-', ' ').split()]) + + @staticmethod + def get_model_id_components(model_id: Optional[str] = None, total_params: int = 0) -> tuple[str | None, str | None, str | None, str | None, str | None, str | None]: + # Huggingface often store model id as '/' + # so let's parse it and apply some heuristics if possible for model name components + + if model_id is None: + # model ID missing + return None, None, None, None, None, None + + if ' ' in model_id: + # model ID is actually a normal human sentence + # which means its most likely a normal model name only + # not part of the hugging face naming standard, but whatever + return model_id, None, None, None, None, None + + if '/' in model_id: + # model ID (huggingface style) + org_component, model_full_name_component = model_id.split('/', 1) + else: + # model ID but missing org components + org_component, model_full_name_component = None, model_id + + # Check if we erroneously matched against './' or '../' etc... + if org_component is not None and len(org_component) > 0 and org_component[0] == '.': + org_component = None + + name_parts: list[str] = model_full_name_component.split('-') + + # Remove empty parts + for i in reversed(range(len(name_parts))): + if len(name_parts[i]) == 0: + del name_parts[i] + + name_types: list[ + set[Literal["basename", "size_label", "finetune", "version", "type"]] + ] = [set() for _ in name_parts] + + # Annotate the name + for i, part in enumerate(name_parts): + # Version + if re.fullmatch(r'(v|iter)?\d+([.]\d+)*', part, re.IGNORECASE): + name_types[i].add("version") + # Quant type (should not be there for base models, but still annotated) + elif re.fullmatch(r'i?q\d(_\w)*|b?fp?(16|32)', part, re.IGNORECASE): + name_types[i].add("type") + name_parts[i] = part.upper() + # Model size + elif i > 0 and re.fullmatch(r'(([A]|\d+[x])?\d+([._]\d+)?[KMBT][\d]?|small|mini|medium|large|x?xl)', part, re.IGNORECASE): + part = part.replace("_", ".") + # Handle weird bloom-7b1 notation + if part[-1].isdecimal(): + part = part[:-2] + "." + part[-1] + part[-2] + # Normalize the size suffixes + if len(part) > 1 and part[-2].isdecimal(): + if part[-1] in "kmbt": + part = part[:-1] + part[-1].upper() + if total_params != 0: + try: + label_params = float(part[:-1]) * pow(1000, " KMBT".find(part[-1])) + # Only use it as a size label if it's close or bigger than the model size + # Note that LoRA adapters don't necessarily include all layers, + # so this is why bigger label sizes are accepted. + # Do not use the size label when it's smaller than 1/8 of the model size + if (total_params < 0 and label_params < abs(total_params) // 8) or ( + # Check both directions when the current model isn't a LoRA adapter + total_params > 0 and abs(label_params - total_params) > 7 * total_params // 8 + ): + # Likely a context length + name_types[i].add("finetune") + # Lowercase the size when it's a context length + part = part[:-1] + part[-1].lower() + except ValueError: + # Failed to convert the size label to float, use it anyway + pass + if len(name_types[i]) == 0: + name_types[i].add("size_label") + name_parts[i] = part + # Some easy to recognize finetune names + elif i > 0 and re.fullmatch(r'chat|instruct|vision|lora', part, re.IGNORECASE): + if total_params < 0 and part.lower() == "lora": + # ignore redundant "lora" in the finetune part when the output is a lora adapter + name_types[i].add("type") + else: + name_types[i].add("finetune") + + # Ignore word-based size labels when there is at least a number-based one present + # TODO: should word-based size labels always be removed instead? + if any(c.isdecimal() for n, t in zip(name_parts, name_types) if "size_label" in t for c in n): + for n, t in zip(name_parts, name_types): + if "size_label" in t: + if all(c.isalpha() for c in n): + t.remove("size_label") + + at_start = True + # Find the basename through the annotated name + for part, t in zip(name_parts, name_types): + if at_start and ((len(t) == 0 and part[0].isalpha()) or "version" in t): + t.add("basename") + else: + if at_start: + at_start = False + if len(t) == 0: + t.add("finetune") + + # Remove the basename annotation from trailing version + for part, t in zip(reversed(name_parts), reversed(name_types)): + if "basename" in t and len(t) > 1: + t.remove("basename") + else: + break + + basename = "-".join(n for n, t in zip(name_parts, name_types) if "basename" in t) or None + # Deduplicate size labels using order-preserving 'dict' ('set' seems to sort the keys) + size_label = "-".join(dict.fromkeys(s for s, t in zip(name_parts, name_types) if "size_label" in t).keys()) or None + finetune = "-".join(f for f, t in zip(name_parts, name_types) if "finetune" in t) or None + # TODO: should the basename version always be excluded? + # NOTE: multiple finetune versions are joined together + version = "-".join(v for v, t, in zip(name_parts, name_types) if "version" in t and "basename" not in t) or None + + if size_label is None and finetune is None and version is None: + # Too ambiguous, output nothing + basename = None + + return model_full_name_component, org_component, basename, finetune, version, size_label + + @staticmethod + def apply_metadata_heuristic(metadata: Metadata, model_card: Optional[dict] = None, hf_params: Optional[dict] = None, model_path: Optional[Path] = None, total_params: int = 0) -> Metadata: + # Reference Model Card Metadata: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1 + + # Model Card Heuristics + ######################## + if model_card is not None: + + def use_model_card_metadata(metadata_key: str, model_card_key: str): + if model_card_key in model_card and getattr(metadata, metadata_key, None) is None: + setattr(metadata, metadata_key, model_card.get(model_card_key)) + + def use_array_model_card_metadata(metadata_key: str, model_card_key: str): + # Note: Will append rather than replace if already exist + tags_value = model_card.get(model_card_key, None) + if tags_value is None: + return + + current_value = getattr(metadata, metadata_key, None) + if current_value is None: + current_value = [] + + if isinstance(tags_value, str): + current_value.append(tags_value) + elif isinstance(tags_value, list): + current_value.extend(tags_value) + + setattr(metadata, metadata_key, current_value) + + # LLAMA.cpp's direct internal convention + # (Definitely not part of hugging face formal/informal standard) + ######################################### + use_model_card_metadata("name", "name") + use_model_card_metadata("author", "author") + use_model_card_metadata("version", "version") + use_model_card_metadata("organization", "organization") + use_model_card_metadata("description", "description") + use_model_card_metadata("finetune", "finetune") + use_model_card_metadata("basename", "basename") + use_model_card_metadata("size_label", "size_label") + use_model_card_metadata("source_url", "url") + use_model_card_metadata("source_doi", "doi") + use_model_card_metadata("source_uuid", "uuid") + use_model_card_metadata("source_repo_url", "repo_url") + + # LLAMA.cpp's huggingface style convention + # (Definitely not part of hugging face formal/informal standard... but with model_ appended to match their style) + ########################################### + use_model_card_metadata("name", "model_name") + use_model_card_metadata("author", "model_author") + use_model_card_metadata("version", "model_version") + use_model_card_metadata("organization", "model_organization") + use_model_card_metadata("description", "model_description") + use_model_card_metadata("finetune", "model_finetune") + use_model_card_metadata("basename", "model_basename") + use_model_card_metadata("size_label", "model_size_label") + use_model_card_metadata("source_url", "model_url") + use_model_card_metadata("source_doi", "model_doi") + use_model_card_metadata("source_uuid", "model_uuid") + use_model_card_metadata("source_repo_url", "model_repo_url") + + # Hugging Face Direct Convention + ################################# + + # Not part of huggingface model card standard but notice some model creator using it + # such as TheBloke in 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF' + use_model_card_metadata("name", "model_name") + use_model_card_metadata("author", "model_creator") + use_model_card_metadata("basename", "model_type") + + if "base_model" in model_card: + # This represents the parent models that this is based on + # Example: stabilityai/stable-diffusion-xl-base-1.0. Can also be a list (for merges) + # Example of merges: https://huggingface.co/EmbeddedLLM/Mistral-7B-Merge-14-v0.1/blob/main/README.md + metadata_base_models = [] + base_model_value = model_card.get("base_model", None) + + if base_model_value is not None: + if isinstance(base_model_value, str): + metadata_base_models.append(base_model_value) + elif isinstance(base_model_value, list): + metadata_base_models.extend(base_model_value) + + if metadata.base_models is None: + metadata.base_models = [] + + for model_id in metadata_base_models: + # NOTE: model size of base model is assumed to be similar to the size of the current model + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + base_model = {} + if model_full_name_component is not None: + base_model["name"] = Metadata.id_to_title(model_full_name_component) + if org_component is not None: + base_model["organization"] = Metadata.id_to_title(org_component) + if version is not None: + base_model["version"] = version + if org_component is not None and model_full_name_component is not None: + base_model["repo_url"] = f"https://huggingface.co/{org_component}/{model_full_name_component}" + metadata.base_models.append(base_model) + + use_model_card_metadata("license", "license") + use_model_card_metadata("license_name", "license_name") + use_model_card_metadata("license_link", "license_link") + + use_array_model_card_metadata("tags", "tags") + use_array_model_card_metadata("tags", "pipeline_tag") + + use_array_model_card_metadata("languages", "languages") + use_array_model_card_metadata("languages", "language") + + use_array_model_card_metadata("datasets", "datasets") + use_array_model_card_metadata("datasets", "dataset") + + # Hugging Face Parameter Heuristics + #################################### + + if hf_params is not None: + + hf_name_or_path = hf_params.get("_name_or_path") + if hf_name_or_path is not None and hf_name_or_path.count('/') <= 1: + # Use _name_or_path only if its actually a model name and not some computer path + # e.g. 'meta-llama/Llama-2-7b-hf' + model_id = hf_name_or_path + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + if metadata.name is None and model_full_name_component is not None: + metadata.name = Metadata.id_to_title(model_full_name_component) + if metadata.organization is None and org_component is not None: + metadata.organization = Metadata.id_to_title(org_component) + if metadata.basename is None and basename is not None: + metadata.basename = basename + if metadata.finetune is None and finetune is not None: + metadata.finetune = finetune + if metadata.version is None and version is not None: + metadata.version = version + if metadata.size_label is None and size_label is not None: + metadata.size_label = size_label + + # Directory Folder Name Fallback Heuristics + ############################################ + if model_path is not None: + model_id = model_path.name + model_full_name_component, org_component, basename, finetune, version, size_label = Metadata.get_model_id_components(model_id, total_params) + if metadata.name is None and model_full_name_component is not None: + metadata.name = Metadata.id_to_title(model_full_name_component) + if metadata.organization is None and org_component is not None: + metadata.organization = Metadata.id_to_title(org_component) + if metadata.basename is None and basename is not None: + metadata.basename = basename + if metadata.finetune is None and finetune is not None: + metadata.finetune = finetune + if metadata.version is None and version is not None: + metadata.version = version + if metadata.size_label is None and size_label is not None: + metadata.size_label = size_label + + return metadata + + def set_gguf_meta_model(self, gguf_writer: gguf.GGUFWriter): + assert self.name is not None + gguf_writer.add_name(self.name) + + if self.author is not None: + gguf_writer.add_author(self.author) + if self.version is not None: + gguf_writer.add_version(self.version) + if self.organization is not None: + gguf_writer.add_organization(self.organization) + + if self.finetune is not None: + gguf_writer.add_finetune(self.finetune) + if self.basename is not None: + gguf_writer.add_basename(self.basename) + + if self.description is not None: + gguf_writer.add_description(self.description) + if self.quantized_by is not None: + gguf_writer.add_quantized_by(self.quantized_by) + + if self.size_label is not None: + gguf_writer.add_size_label(self.size_label) + + if self.license is not None: + gguf_writer.add_license(self.license) + if self.license_name is not None: + gguf_writer.add_license_name(self.license_name) + if self.license_link is not None: + gguf_writer.add_license_link(self.license_link) + + if self.url is not None: + gguf_writer.add_url(self.url) + if self.doi is not None: + gguf_writer.add_doi(self.doi) + if self.uuid is not None: + gguf_writer.add_uuid(self.uuid) + if self.repo_url is not None: + gguf_writer.add_repo_url(self.repo_url) + + if self.source_url is not None: + gguf_writer.add_source_url(self.source_url) + if self.source_doi is not None: + gguf_writer.add_source_doi(self.source_doi) + if self.source_uuid is not None: + gguf_writer.add_source_uuid(self.source_uuid) + if self.source_repo_url is not None: + gguf_writer.add_source_repo_url(self.source_repo_url) + + if self.base_models is not None: + gguf_writer.add_base_model_count(len(self.base_models)) + for key, base_model_entry in enumerate(self.base_models): + if "name" in base_model_entry: + gguf_writer.add_base_model_name(key, base_model_entry["name"]) + if "author" in base_model_entry: + gguf_writer.add_base_model_author(key, base_model_entry["author"]) + if "version" in base_model_entry: + gguf_writer.add_base_model_version(key, base_model_entry["version"]) + if "organization" in base_model_entry: + gguf_writer.add_base_model_organization(key, base_model_entry["organization"]) + if "url" in base_model_entry: + gguf_writer.add_base_model_url(key, base_model_entry["url"]) + if "doi" in base_model_entry: + gguf_writer.add_base_model_doi(key, base_model_entry["doi"]) + if "uuid" in base_model_entry: + gguf_writer.add_base_model_uuid(key, base_model_entry["uuid"]) + if "repo_url" in base_model_entry: + gguf_writer.add_base_model_repo_url(key, base_model_entry["repo_url"]) + + if self.tags is not None: + gguf_writer.add_tags(self.tags) + if self.languages is not None: + gguf_writer.add_languages(self.languages) + if self.datasets is not None: + gguf_writer.add_datasets(self.datasets) diff --git a/vllm/lib/python3.10/site-packages/gguf/py.typed b/vllm/lib/python3.10/site-packages/gguf/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vllm/lib/python3.10/site-packages/gguf/quants.py b/vllm/lib/python3.10/site-packages/gguf/quants.py new file mode 100644 index 0000000000000000000000000000000000000000..ff589b85245e5f4a3dfda72aae6402b523459dea --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/quants.py @@ -0,0 +1,1188 @@ +from __future__ import annotations +from abc import ABC, abstractmethod +from typing import Any, Callable, Sequence +from math import log2, ceil + +from numpy.typing import DTypeLike + +from .constants import GGML_QUANT_SIZES, GGMLQuantizationType, QK_K +from .lazy import LazyNumpyTensor + +import numpy as np + + +def quant_shape_to_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]: + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % block_size != 0: + raise ValueError(f"Quantized tensor row size ({shape[-1]}) is not a multiple of {quant_type.name} block size ({block_size})") + return (*shape[:-1], shape[-1] // block_size * type_size) + + +def quant_shape_from_byte_shape(shape: Sequence[int], quant_type: GGMLQuantizationType) -> tuple[int, ...]: + block_size, type_size = GGML_QUANT_SIZES[quant_type] + if shape[-1] % type_size != 0: + raise ValueError(f"Quantized tensor bytes per row ({shape[-1]}) is not a multiple of {quant_type.name} type size ({type_size})") + return (*shape[:-1], shape[-1] // type_size * block_size) + + +# This is faster than np.vectorize and np.apply_along_axis because it works on more than one row at a time +def _apply_over_grouped_rows(func: Callable[[np.ndarray], np.ndarray], arr: np.ndarray, otype: DTypeLike, oshape: tuple[int, ...]) -> np.ndarray: + rows = arr.reshape((-1, arr.shape[-1])) + osize = 1 + for dim in oshape: + osize *= dim + out = np.empty(shape=osize, dtype=otype) + # compute over groups of 16 rows (arbitrary, but seems good for performance) + n_groups = (rows.shape[0] // 16) or 1 + np.concatenate([func(group).ravel() for group in np.array_split(rows, n_groups)], axis=0, out=out) + return out.reshape(oshape) + + +# round away from zero +# ref: https://stackoverflow.com/a/59143326/22827863 +def np_roundf(n: np.ndarray) -> np.ndarray: + a = abs(n) + floored = np.floor(a) + b = floored + np.floor(2 * (a - floored)) + return np.sign(n) * b + + +class QuantError(Exception): ... + + +_type_traits: dict[GGMLQuantizationType, type[__Quant]] = {} + + +def quantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + if qtype == GGMLQuantizationType.F32: + return data.astype(np.float32, copy=False) + elif qtype == GGMLQuantizationType.F16: + return data.astype(np.float16, copy=False) + elif (q := _type_traits.get(qtype)) is not None: + return q.quantize(data) + else: + raise NotImplementedError(f"Quantization for {qtype.name} is not yet implemented") + + +def dequantize(data: np.ndarray, qtype: GGMLQuantizationType) -> np.ndarray: + if qtype == GGMLQuantizationType.F32: + return data.view(np.float32) + elif qtype == GGMLQuantizationType.F16: + return data.view(np.float16).astype(np.float32) + elif (q := _type_traits.get(qtype)) is not None: + return q.dequantize(data) + else: + raise NotImplementedError(f"Dequantization for {qtype.name} is not yet implemented") + + +class __Quant(ABC): + qtype: GGMLQuantizationType + block_size: int + type_size: int + + grid: np.ndarray[Any, np.dtype[np.float32]] | None = None + grid_shape: tuple[int, int] = (0, 0) + grid_map: tuple[int | float, ...] = () + grid_hex: bytes | None = None + + def __init__(self): + return TypeError("Quant conversion classes can't have instances") + + def __init_subclass__(cls, qtype: GGMLQuantizationType) -> None: + cls.qtype = qtype + cls.block_size, cls.type_size = GGML_QUANT_SIZES[qtype] + cls.__quantize_lazy = LazyNumpyTensor._wrap_fn( + cls.__quantize_array, + meta_noop=(np.uint8, cls.__shape_to_bytes) + ) + cls.__dequantize_lazy = LazyNumpyTensor._wrap_fn( + cls.__dequantize_array, + meta_noop=(np.float32, cls.__shape_from_bytes) + ) + assert qtype not in _type_traits + _type_traits[qtype] = cls + + @classmethod + def init_grid(cls): + if cls.grid is not None or cls.grid_hex is None: + return + + bits_per_elem = ceil(log2(len(cls.grid_map))) + assert bits_per_elem != 0, cls.qtype.name + elems_per_byte = 8 // bits_per_elem + + grid = np.frombuffer(cls.grid_hex, dtype=np.uint8) + # decode hexadecimal chars from grid + grid = grid.reshape((-1, 2)) + grid = (np.where(grid > 0x40, grid + 9, grid) & 0x0F) << np.array([4, 0], dtype=np.uint8).reshape((1, 2)) + grid = grid[..., 0] | grid[..., 1] + # unpack the grid values + grid = grid.reshape((-1, 1)) >> np.array([i for i in range(0, 8, 8 // elems_per_byte)], dtype=np.uint8).reshape((1, elems_per_byte)) + grid = (grid & ((1 << bits_per_elem) - 1)).reshape((-1, 1)) + grid_map = np.array(cls.grid_map, dtype=np.float32).reshape((1, -1)) + grid = np.take_along_axis(grid_map, grid, axis=-1) + cls.grid = grid.reshape((1, 1, *cls.grid_shape)) + + @classmethod + @abstractmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + raise NotImplementedError + + @classmethod + @abstractmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + raise NotImplementedError + + @classmethod + def quantize_rows(cls, rows: np.ndarray) -> np.ndarray: + rows = rows.astype(np.float32, copy=False) + shape = rows.shape + n_blocks = rows.size // cls.block_size + blocks = rows.reshape((n_blocks, cls.block_size)) + blocks = cls.quantize_blocks(blocks) + assert blocks.dtype == np.uint8 + assert blocks.shape[-1] == cls.type_size + return blocks.reshape(cls.__shape_to_bytes(shape)) + + @classmethod + def dequantize_rows(cls, rows: np.ndarray) -> np.ndarray: + rows = rows.view(np.uint8) + shape = rows.shape + n_blocks = rows.size // cls.type_size + blocks = rows.reshape((n_blocks, cls.type_size)) + blocks = cls.dequantize_blocks(blocks) + assert blocks.dtype == np.float32 + assert blocks.shape[-1] == cls.block_size + return blocks.reshape(cls.__shape_from_bytes(shape)) + + @classmethod + def __shape_to_bytes(cls, shape: Sequence[int]): + return quant_shape_to_byte_shape(shape, cls.qtype) + + @classmethod + def __shape_from_bytes(cls, shape: Sequence[int]): + return quant_shape_from_byte_shape(shape, cls.qtype) + + @classmethod + def __quantize_array(cls, array: np.ndarray) -> np.ndarray: + return _apply_over_grouped_rows(cls.quantize_rows, arr=array, otype=np.uint8, oshape=cls.__shape_to_bytes(array.shape)) + + @classmethod + def __dequantize_array(cls, array: np.ndarray) -> np.ndarray: + cls.init_grid() + return _apply_over_grouped_rows(cls.dequantize_rows, arr=array, otype=np.float32, oshape=cls.__shape_from_bytes(array.shape)) + + @classmethod + def __quantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any: + pass + + @classmethod + def __dequantize_lazy(cls, lazy_tensor: LazyNumpyTensor, /) -> Any: + pass + + @classmethod + def can_quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> bool: + return tensor.shape[-1] % cls.block_size == 0 + + @classmethod + def quantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray: + if not cls.can_quantize(tensor): + raise QuantError(f"Can't quantize tensor with shape {tensor.shape} to {cls.qtype.name}") + if isinstance(tensor, LazyNumpyTensor): + return cls.__quantize_lazy(tensor) + else: + return cls.__quantize_array(tensor) + + @classmethod + def dequantize(cls, tensor: np.ndarray | LazyNumpyTensor) -> np.ndarray: + if isinstance(tensor, LazyNumpyTensor): + return cls.__dequantize_lazy(tensor) + else: + return cls.__dequantize_array(tensor) + + +class BF16(__Quant, qtype=GGMLQuantizationType.BF16): + @classmethod + # same as ggml_compute_fp32_to_bf16 in ggml-impl.h + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n = blocks.view(np.uint32) + # force nan to quiet + n = np.where((n & 0x7fffffff) > 0x7f800000, (n & np.uint32(0xffff0000)) | np.uint32(64 << 16), n) + # round to nearest even + n = (np.uint64(n) + (0x7fff + ((n >> 16) & 1))) >> 16 + return n.astype(np.uint16).view(np.uint8) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + return (blocks.view(np.int16).astype(np.int32) << 16).view(np.float32) + + +class Q4_0(__Quant, qtype=GGMLQuantizationType.Q4_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + imax = abs(blocks).argmax(axis=-1, keepdims=True) + max = np.take_along_axis(blocks, imax, axis=-1) + + d = max / -8 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + # FIXME: Q4_0's reference rounding is cursed and depends on FMA + qs = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(8.5), dtype=np.float32).astype(np.uint8).clip(0, 15) + + qs = qs.reshape((n_blocks, 2, cls.block_size // 2)) + qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4)) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([d, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.int8) - np.int8(8) + + return (d * qs.astype(np.float32)) + + +class Q4_1(__Quant, qtype=GGMLQuantizationType.Q4_1): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + max = blocks.max(axis=-1, keepdims=True) + min = blocks.min(axis=-1, keepdims=True) + + d = (max - min) / 15 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 15) + + qs = qs.reshape((n_blocks, 2, cls.block_size // 2)) + qs = qs[..., 0, :] | (qs[..., 1, :] << np.uint8(4)) + + d = d.astype(np.float16).view(np.uint8) + m = min.astype(np.float16).view(np.uint8) + + return np.concatenate([d, m, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + m, qs = np.hsplit(rest, [2]) + + d = d.view(np.float16).astype(np.float32) + m = m.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1)).astype(np.float32) + + return (d * qs) + m + + +class Q5_0(__Quant, qtype=GGMLQuantizationType.Q5_0): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + imax = abs(blocks).argmax(axis=-1, keepdims=True) + max = np.take_along_axis(blocks, imax, axis=-1) + + d = max / -16 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + # FIXME: Q5_0's reference rounding is cursed and depends on FMA + q = np.trunc((np.float64(blocks) * np.float64(id)) + np.float64(16.5), dtype=np.float32).astype(np.uint8).clip(0, 31) + + qs = q.reshape((n_blocks, 2, cls.block_size // 2)) + qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4)) + + qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4) + + d = d.astype(np.float16).view(np.uint8) + + return np.concatenate([d, qh, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qh, qs = np.hsplit(rest, [4]) + + d = d.view(np.float16).astype(np.float32) + qh = qh.view(np.uint32) + + qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32)) + ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = (qh & np.uint32(0x01)).astype(np.uint8) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1)) + + qs = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(16) + + return (d * qs.astype(np.float32)) + + +class Q5_1(__Quant, qtype=GGMLQuantizationType.Q5_1): + @classmethod + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + max = blocks.max(axis=-1, keepdims=True) + min = blocks.min(axis=-1, keepdims=True) + + d = (max - min) / 31 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + q = np.trunc((blocks - min) * id + np.float32(0.5), dtype=np.float32).astype(np.uint8).clip(0, 31) + + qs = q.reshape((n_blocks, 2, cls.block_size // 2)) + qs = (qs[..., 0, :] & np.uint8(0x0F)) | (qs[..., 1, :] << np.uint8(4)) + + qh = np.packbits(q.reshape((n_blocks, 1, 32)) >> np.uint8(4), axis=-1, bitorder="little").reshape(n_blocks, 4) + + d = d.astype(np.float16).view(np.uint8) + m = min.astype(np.float16).view(np.uint8) + + return np.concatenate([d, m, qh, qs], axis=-1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + m, rest = np.hsplit(rest, [2]) + qh, qs = np.hsplit(rest, [4]) + + d = d.view(np.float16).astype(np.float32) + m = m.view(np.float16).astype(np.float32) + qh = qh.view(np.uint32) + + qh = qh.reshape((n_blocks, 1)) >> np.array([i for i in range(32)], dtype=np.uint32).reshape((1, 32)) + ql = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = (qh & np.uint32(0x01)).astype(np.uint8) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1)) + + qs = (ql | (qh << np.uint8(4))).astype(np.float32) + + return (d * qs) + m + + +class Q8_0(__Quant, qtype=GGMLQuantizationType.Q8_0): + @classmethod + # Implementation of Q8_0 with bit-exact same results as reference implementation in ggml-quants.c + def quantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + + d = abs(blocks).max(axis=1, keepdims=True) / 127 + with np.errstate(divide="ignore"): + id = np.where(d == 0, 0, 1 / d) + qs = np_roundf(blocks * id) + + # (n_blocks, 2) + d = d.astype(np.float16).view(np.uint8) + # (n_blocks, block_size) + qs = qs.astype(np.int8).view(np.uint8) + + return np.concatenate([d, qs], axis=1) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + d, x = np.split(blocks, [2], axis=1) + d = d.view(np.float16).astype(np.float32) + x = x.view(np.int8).astype(np.float32) + + return (x * d) + + +class Q2_K(__Quant, qtype=GGMLQuantizationType.Q2_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + scales, rest = np.hsplit(blocks, [QK_K // 16]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + d, dmin = np.hsplit(rest, [2]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + # (n_blocks, 16, 1) + dl = (d * (scales & 0xF).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1)) + ml = (dmin * (scales >> 4).astype(np.float32)).reshape((n_blocks, QK_K // 16, 1)) + + shift = np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + + qs = (qs.reshape((n_blocks, -1, 1, 32)) >> shift) & np.uint8(3) + + qs = qs.reshape((n_blocks, QK_K // 16, 16)).astype(np.float32) + + qs = dl * qs - ml + + return qs.reshape((n_blocks, -1)) + + +class Q3_K(__Quant, qtype=GGMLQuantizationType.Q3_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + hmask, rest = np.hsplit(blocks, [QK_K // 8]) + qs, rest = np.hsplit(rest, [QK_K // 4]) + scales, d = np.hsplit(rest, [12]) + + d = d.view(np.float16).astype(np.float32) + + # The scales are packed at 6-bit each in this pattern: + # 0: IIIIAAAA + # 1: JJJJBBBB + # 2: KKKKCCCC + # 3: LLLLDDDD + # 4: MMMMEEEE + # 5: NNNNFFFF + # 6: OOOOGGGG + # 7: PPPPHHHH + # 8: MMIIEEAA + # 9: NNJJFFBB + # 10: OOKKGGCC + # 11: PPLLHHDD + lscales, hscales = np.hsplit(scales, [8]) + lscales = lscales.reshape((n_blocks, 1, 8)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 2, 1)) + lscales = lscales.reshape((n_blocks, 16)) + hscales = hscales.reshape((n_blocks, 1, 4)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 4, 1)) + hscales = hscales.reshape((n_blocks, 16)) + scales = (lscales & np.uint8(0x0F)) | ((hscales & np.uint8(0x03)) << np.uint8(4)) + scales = (scales.astype(np.int8) - np.int8(32)).astype(np.float32) + + dl = (d * scales).reshape((n_blocks, 16, 1)) + + ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = hmask.reshape(n_blocks, -1, 1, 32) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1)) + ql = ql.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(3) + qh = (qh.reshape((n_blocks, 16, QK_K // 16)) & np.uint8(1)) + qh = qh ^ np.uint8(1) # strangely, the offset is zero when the bitmask is 1 + q = (ql.astype(np.int8) - (qh << np.uint8(2)).astype(np.int8)).astype(np.float32) + + return (dl * q).reshape((n_blocks, QK_K)) + + +class Q4_K(__Quant, qtype=GGMLQuantizationType.Q4_K): + K_SCALE_SIZE = 12 + + @staticmethod + def get_scale_min(scales: np.ndarray) -> tuple[np.ndarray, np.ndarray]: + n_blocks = scales.shape[0] + scales = scales.view(np.uint8) + ### Unpacking the following: ### + # 0 EEAAAAAA + # 1 FFBBBBBB + # 2 GGCCCCCC + # 3 HHDDDDDD + # 4 eeaaaaaa + # 5 ffbbbbbb + # 6 ggcccccc + # 7 hhdddddd + # 8 eeeeEEEE + # 9 ffffFFFF + # 10 ggggGGGG + # 11 hhhhHHHH + scales = scales.reshape((n_blocks, 3, 4)) + d, m, m_d = np.split(scales, 3, axis=-2) + + sc = np.concatenate([d & 0x3F, (m_d & 0x0F) | ((d >> 2) & 0x30)], axis=-1) + min = np.concatenate([m & 0x3F, (m_d >> 4) | ((m >> 2) & 0x30)], axis=-1) + + return (sc.reshape((n_blocks, 8)), min.reshape((n_blocks, 8))) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + dmin, rest = np.hsplit(rest, [2]) + scales, qs = np.hsplit(rest, [cls.K_SCALE_SIZE]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + sc, m = Q4_K.get_scale_min(scales) + + d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1)) + dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1)) + + qs = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 32)).astype(np.float32) + + return (d * qs - dm).reshape((n_blocks, QK_K)) + + +class Q5_K(__Quant, qtype=GGMLQuantizationType.Q5_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + dmin, rest = np.hsplit(rest, [2]) + scales, rest = np.hsplit(rest, [Q4_K.K_SCALE_SIZE]) + qh, qs = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + dmin = dmin.view(np.float16).astype(np.float32) + + sc, m = Q4_K.get_scale_min(scales) + + d = (d * sc.astype(np.float32)).reshape((n_blocks, -1, 1)) + dm = (dmin * m.astype(np.float32)).reshape((n_blocks, -1, 1)) + + ql = qs.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8, 1)) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32)) + qh = (qh & np.uint8(0x01)).reshape((n_blocks, -1, 32)) + q = (ql | (qh << np.uint8(4))).astype(np.float32) + + return (d * q - dm).reshape((n_blocks, QK_K)) + + +class Q6_K(__Quant, qtype=GGMLQuantizationType.Q6_K): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + ql, rest = np.hsplit(blocks, [QK_K // 2]) + qh, rest = np.hsplit(rest, [QK_K // 4]) + scales, d = np.hsplit(rest, [QK_K // 16]) + + scales = scales.view(np.int8).astype(np.float32) + d = d.view(np.float16).astype(np.float32) + d = (d * scales).reshape((n_blocks, QK_K // 16, 1)) + + ql = ql.reshape((n_blocks, -1, 1, 64)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + ql = (ql & np.uint8(0x0F)).reshape((n_blocks, -1, 32)) + qh = qh.reshape((n_blocks, -1, 1, 32)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4, 1)) + qh = (qh & np.uint8(0x03)).reshape((n_blocks, -1, 32)) + q = (ql | (qh << np.uint8(4))).astype(np.int8) - np.int8(32) + q = q.reshape((n_blocks, QK_K // 16, -1)).astype(np.float32) + + return (d * q).reshape((n_blocks, QK_K)) + + +class IQ2_XXS(__Quant, qtype=GGMLQuantizationType.IQ2_XXS): + ksigns: bytes = ( + b"\x00\x81\x82\x03\x84\x05\x06\x87\x88\x09\x0a\x8b\x0c\x8d\x8e\x0f" + b"\x90\x11\x12\x93\x14\x95\x96\x17\x18\x99\x9a\x1b\x9c\x1d\x1e\x9f" + b"\xa0\x21\x22\xa3\x24\xa5\xa6\x27\x28\xa9\xaa\x2b\xac\x2d\x2e\xaf" + b"\x30\xb1\xb2\x33\xb4\x35\x36\xb7\xb8\x39\x3a\xbb\x3c\xbd\xbe\x3f" + b"\xc0\x41\x42\xc3\x44\xc5\xc6\x47\x48\xc9\xca\x4b\xcc\x4d\x4e\xcf" + b"\x50\xd1\xd2\x53\xd4\x55\x56\xd7\xd8\x59\x5a\xdb\x5c\xdd\xde\x5f" + b"\x60\xe1\xe2\x63\xe4\x65\x66\xe7\xe8\x69\x6a\xeb\x6c\xed\xee\x6f" + b"\xf0\x71\x72\xf3\x74\xf5\xf6\x77\x78\xf9\xfa\x7b\xfc\x7d\x7e\xff" + ) + + # iq2xxs_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (256, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a00110014002000220028002a0041004400500058006100" + b"6400800082008a00a20001010401100115014001840198010002020222028202" + b"010404041004210424044004420448046004810484049004a404000502050805" + b"200546056905800591050906100640068406a406000805080808140828084108" + b"440850085208880804094009020a140a01100410101021104010601084109010" + b"951000110811201150115a118011241245120014081420142514491480141815" + b"6215001616160118041810184018811800190519a019511a002002200a204420" + b"6120802082202921482100220222012404241024402456240025412564259026" + b"082820289428442a014004401040184021402440404048405640604081408440" + b"9040004120416141804185410142104248425642684200440844204480449944" + b"124524450046014804481048404845480049584961498249454a904a00500850" + b"1150195020508050885004514251a4519152905492540a550156545600581158" + b"195864584059085a046010604060686000615561186260620064056410651265" + b"84654268008002800a8041808280048118814081118201840484108415844084" + b"608400854685948509864086608602880489118a0490109024904090a1901691" + b"8091459200942294449451958198209902a050a085a009a100a218a450a804a9" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.view(np.uint32).reshape(n_blocks, -1, 2) + + db = d * (np.float32(0.5) + (qs[..., 1] >> 28).astype(np.float32)) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = qs[..., 1].reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4)) + ksigns = np.frombuffer(cls.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128)) + signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1)) + signs = np.take_along_axis(ksigns, signs, axis=-1) + signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs[..., 0].copy().view(np.uint8).reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ2_XS(__Quant, qtype=GGMLQuantizationType.IQ2_XS): + # iq2xs_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (512, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a0011001400160019002000220025002800410044004600" + b"49005000520055005800610064008000820085008800910094009900a0000101" + b"04010601090110011201150118011a0121012401400142014501480151015401" + b"6001680181018401900100020202050208021102140220024102440250025502" + b"80028a0201040404060409041004120415041804210424044004420445044804" + b"5104540456046004810484049004000502050505080511051405200541054405" + b"500561058005010604061006260640064206840600080208050808080a081108" + b"14082008250841084408500858088008a008aa08010904091009400981098909" + b"000a200a280a960aa00a01100410061009101010121015101810211024104010" + b"4210451048105110541060106a10811084109010001102110511081111111411" + b"2011411144115011801194119611011204120612101240126012001402140514" + b"0814111414142014411444144914501464148014011504151015401500161416" + b"49160118041810181218401854188618001905196619511aa91a002002200520" + b"08200a201120142020204120442050208020a020012104211021402148216521" + b"002222228022a82201240424102429244024002541255225992501261a26a626" + b"002808280a28202855288828a22868299029082a202a822a882a8a2a01400440" + b"0640094010401240154018402140244040404240454048404a40514054406040" + b"6540814084409040004102410541084111411441204141414441504180418541" + b"a241014204421042124229424042004402440544084411441444194420444144" + b"4444504480449444014504451045244540459a4500460a464446504601480448" + b"1048404845485448624800491149444950496949044a00500250055008501150" + b"145020502850415044505050805001510451105115514051425100524452aa52" + b"0154045410542154405460548154a154005508558055885521566856a1560058" + b"14584158505899581a5940594259855a0160046010604060546062608660a960" + b"006124624a62926200641664106540654565a46501686a682569066a546a626a" + b"00800280058008801180148020802a8041804480508080808280a880aa800181" + b"0481068110814081518159810082208280828282a082a8820184048410841284" + b"158440846084898400854485a58518866a860088088825885a8880888288a888" + b"0689228a808a888a968aa88a0190049010904090569084900091229164915692" + b"89920094059444945094589429959095929541965198a6984999159a609a00a0" + b"02a008a00aa020a02aa0a0a051a159a1a6a100a202a208a22aa280a2a0a240a4" + b"95a465a698a60aa820a822a828a8a0a8a8a804a984a986a928aa2aaa91aaaaaa" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, scales = np.hsplit(rest, [2 * QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + qs = qs.view(np.uint16) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (np.float32(0.5) + scales) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape(1, 1, 128) + signs = np.take_along_axis(signs, (qs >> 9).reshape((n_blocks, -1, 1)), axis=-1) + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 2, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, (qs & np.uint16(511)).reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ2_S(__Quant, qtype=GGMLQuantizationType.IQ2_S): + # iq2s_grid, but with each byte of the original packed in 2 bits, + # by mapping 0x08 to 0, 0x19 to 1, and 0x2b to 2. + grid_shape = (1024, 8) + grid_map = (0x08, 0x19, 0x2b) + grid_hex = ( + b"00000200050008000a0011001400160019002000220025002800410044004600" + b"490050005200550058006100640066006900800082008500880091009400a000" + b"a500aa0001010401060109011001120115011801210124014001420145014801" + b"510154015601590160016501680181018401900192019501a101a40100020202" + b"050208021102140220022a02410244024602490250025502800285028a029402" + b"a202010404040604090410041204150418042104240426042904400442044504" + b"48044a0451045404560459046004620465048104840486048904900495049804" + b"a104a40400050205050508050a05110514051605190520052505280541054405" + b"46054905500552055505580561056405800582058505880591059405a0050106" + b"0406060609061006150640064506480651065406600681068406900600080208" + b"050808081108140816081908200825082a084108440846084908500852085508" + b"580861086408800885089408aa08010904091009120915091809210940094509" + b"480951095409600981099009000a110a140a220a280a2a0a500a990a01100410" + b"0610091010101210151018102110241026104010421045104810511054105610" + b"59106010621065106810811084108610901095109810a110a410001102110511" + b"08110a1111111411161119112011221125112811411144114611491150115211" + b"5511581161116411801182118511881191119411011204120912101215122112" + b"2412401245125112541281128412901200140214051408141114141416141914" + b"2014251428144114441446144914501452145514581461146414801482148514" + b"881491149414a014011504150615091510151215151518152115241540154215" + b"4515481551155415601581158415901500160516081611161416201641164416" + b"50168016aa160118041806180918101815181818211840184218451848185118" + b"541860188118841800190219051908191119141920194119441950196919a219" + b"041a101a401a561a00200220052008201120142016201920202025202a204120" + b"4420502052205520642080208a209420aa200121042110211221152121214021" + b"4221452151215421602181218421902100220a22222228222a22442250228822" + b"8a22a82201240424062409241024152418242124242440244224452448245124" + b"5424602481248424902400250525082511251425202541254425502566258025" + b"0126042610264026592600280528112814284128442850288a28aa2801290429" + b"102995290a2a222a642a882a8a2a014004400640094010401240154018401a40" + b"21402440264040404240454048404a4051405440564059406040624065408140" + b"8440904095409840a140a4400041024105410841114114411641194120412241" + b"2541414144414641494150415241554158416141644180418241854188419141" + b"9441a04101420442104212421542184224424042454248425142544260428142" + b"844200440244054408440a441144144416441944204422442544284441444444" + b"46444944504452445544584461446444804482448544884491449444a0440145" + b"0445064509451045124515451845214524454045424545454845514554456045" + b"6a4581458445904500460246054608461146144620464146444650468046a546" + b"0148044809481048124815481848214824484048424845484848514854486048" + b"84489048004902490549084911491449204941494449504980499649014a044a" + b"104a404a00500250055008501150145016501950205022502550285041504450" + b"4650495050505250555058506150645080508250855088509150945001510451" + b"0651095110511251155118512151245140514251455148515151545160518151" + b"8451905100520552085211521452205241524452505269528052015404540654" + b"0954105412541554185421542454405442544554485451545454605481548454" + b"9054005502550555085511551455205541554455505580550156045610562656" + b"405600580258055808581158145820584158445850585a588058015904591059" + b"4059005a195a855aa85a01600460066010601260156018602160246040604560" + b"4860516054606060846090600061026105610861116114612061416144615061" + b"806199610462106240625662a162006405640864116414642064416444645064" + b"806401650465106540654a656865926500669466016804681068656898680069" + b"2a69426aa16a0080028005800880118014801980208025804180448050805280" + b"5580588061808080858091809480018104810981108112811581188121812481" + b"408142814581488151815481818184819081a981008205820a82118214824182" + b"4482508201840484068409841084128415841884218440844284458448845184" + b"5484608481848484908400850285058508851185148520854185448550858085" + b"8a85018604861086298640860088058811881488418844885088a28801890489" + b"40896589228a588a5a8a828aa28a019004900990109012901590189024904090" + b"4290459048905190549060908190849090900091059111911491419144915091" + b"5a910192049210924092a6920094029405940894119414942094419444945094" + b"8094969401950495109540959895a19500964696649601980498109826984098" + b"a998009949995299909a00a005a00aa014a022a02aa041a044a050a0a2a0aaa0" + b"40a165a102a20aa222a228a22aa282a288a28aa2a8a201a404a410a440a489a4" + b"a4a400a519a551a60aa828a8a2a854a986a908aa0aaa20aa22aa28aa88aaaaaa" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, rest = np.hsplit(rest, [QK_K // 8]) + signs, rest = np.hsplit(rest, [QK_K // 8]) + qh, scales = np.hsplit(rest, [QK_K // 32]) + + d = d.view(np.float16).astype(np.float32) + + scales = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (np.float32(0.5) + scales) * np.float32(0.25) + db = db.reshape((n_blocks, -1, 1, 1)) + + # unpack the sign bits + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 2, 8)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 2, 4, 6], dtype=np.uint8).reshape((1, 1, 4)) + qs = qs.astype(np.uint16) | ((qh & 0x03).astype(np.uint16) << 8).reshape((n_blocks, -1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ3_XXS(__Quant, qtype=GGMLQuantizationType.IQ3_XXS): + grid_shape = (256, 4) + grid_map = (0x04, 0x0c, 0x14, 0x1c, 0x24, 0x2c, 0x34, 0x3e) + grid_hex = ( + b"0000020004001100130017002000220031004200730075000101030110011201" + b"2101250130013201410154017001000202020402110220022202310233023702" + b"5102570275020103070310031203250370031304370444045704730475040105" + b"0705320552053506640610071407160743076107011003101010121021102310" + b"3010321034104710501000110211111120112211011203121012121221123012" + b"7212001302132013311346136613011405145014201524154615711505162217" + b"4017002002201120132020202220262031204220012103210521102112212121" + b"3021632167217021002202221122172220222222372240225522012310231423" + b"7023742335245324032527254125742501270327162745270130103012302130" + b"2330503065307230003102312031313144314631013203321032253252327232" + b"1133333330344734723400350635223555351436363663363337603704401740" + b"3540374053405740744120423742404260426642074345430444514464442545" + b"4345704505471047124730471250415070500051065126515551145232527252" + b"0253535310542354275472540255315550562457425724604460466064602161" + b"6161176264623063366344640565526533660367216703700570077010703270" + b"5270267140711272457252720073157333736073217441740075027524753076" + ) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, scales = np.hsplit(rest, [QK_K // 4]) + + d = d.view(np.float16).astype(np.float32) + scales = scales.view(np.uint32) + + db = d * (np.float32(0.5) + (scales >> 28).astype(np.float32)) * np.float32(0.5) + db = db.reshape((n_blocks, -1, 1, 1)) + + # get the sign indices and unpack the bits + signs = scales.reshape((n_blocks, -1, 1)) >> np.array([0, 7, 14, 21], dtype=np.uint32).reshape((1, 1, 4)) + ksigns = np.frombuffer(IQ2_XXS.ksigns, dtype=np.uint8).reshape((1, 1, 1, 128)) + signs = (signs & np.uint32(0x7F)).reshape((n_blocks, -1, 4, 1)) + signs = np.take_along_axis(ksigns, signs, axis=-1) + signs = signs.reshape((n_blocks, -1, 4, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ3_S(__Quant, qtype=GGMLQuantizationType.IQ3_S): + grid_shape = (512, 4) + grid_map = (0x01, 0x03, 0x05, 0x07, 0x09, 0x0b, 0x0d, 0x0f) + grid_hex = ( + b"0000010002000500070010001100120014001600200021002500330040004200" + b"4500470051005300600062007100740077000001010102010401100111011501" + b"2001230127013101350144016101650172010002010205020702100213021602" + b"2102250230023402420245024702510253027002730203031103150320032203" + b"3103330336034403500352036703710375030004130417042104240432044004" + b"4304510470040205040520052205260533054105450547056605730506061106" + b"1306310652067106000702070407200722072607330750075407001001100210" + b"0410101011101310151017102010221031103410361054105610611072100011" + b"0111031106111011141121113011331141115011521170117611001212121512" + b"1712201224123212401243125512601272120113041307131013131321132713" + b"3013341341136213701303140514121414143114331442144614501454140115" + b"1015131521153015321551152016241627164416461601170317101712172117" + b"3517411762177017002001200320052007201020122014201620212023202720" + b"3020322041204320452050205220672070207320752000210221102113211721" + b"2221252131213421422151210122042207222122232230223722412253225722" + b"7122742200230223052311232223242331233323422350236623012407242024" + b"2324322435244124722475240425112522253725402553257025002602260726" + b"2126552661260527112726273027432750270230113013301530173022303130" + b"3330353042304430473051306330713001310331053114312131233140316031" + b"7231763100321232203232323432503201331033143321332333273330334133" + b"4333473355337333033411341634223431345234603464340135103512352535" + b"3235443556357335163641360137033720372237353700400440124020402440" + b"2740324041405040704002410741114113412241304135414341514155410142" + 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1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales = (scales & 0x0F).reshape((n_blocks, -1)) + db = d * (1 + 2 * scales) + db = db.reshape((n_blocks, -1, 1, 1)) + + # unpack the sign bits + signs = signs.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8).reshape((1, 1, 8)) + signs = signs & np.uint8(0x01) + signs = np.where(signs == 0, np.float32(1), np.float32(-1)) + signs = signs.reshape((n_blocks, -1, 4, 8)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([i for i in range(8)], dtype=np.uint8) + qh = (qh & 0x01).astype(np.uint16).reshape((n_blocks, -1)) + qs = qs.astype(np.uint16) | (qh << 8) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (db * grid * signs).reshape((n_blocks, -1)) + + +class IQ1_S(__Quant, qtype=GGMLQuantizationType.IQ1_S): + # iq1s_grid, with each byte packed into 2 bits + # -1, 0, 1 <=> 0, 1, 2 + grid_shape = (2048, 8) + grid_map = (-1, 0, 1) + grid_hex = ( + b"00000200050008000a00110015002000220028002a0045005100540056006500" + b"8000820088008a009500a000a200a800aa000401050111011401160119011a01" + b"2501410146014901520155015a0161016401660168018501910194019601a501" + b"0002020208020a0215022002220228022a024502510259026402690280028202" + b"88028a02910295029902a002a202a802aa021104140416042504410449045504" + b"5a046404650491049904a5040105040505050605150518051a05290540054505" + b"4a0550055105540555055605590560056205650568056a058105910595059805" + b"9a05a105a405a505a605a9051406190641064406500652065506580660066106" + b"6606690685069106940699060008020808080a0815082008220828082a084508" + b"5108560865088008820888088a089508a008a208a808aa080509110914091909" + b"2409250941095009510955096109640969099109940996099909a509000a020a" + b"080a0a0a150a200a220a280a2a0a450a510a590a610a650a800a820a850a880a" + b"8a0a950aa00aa20aa80aaa0a1010111014101910241025104110441050105510" + 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b"2696299645964896499651965296559656965996659668968296849689968a96" + b"929694969596a496a696a9960598169819982598419846985098529855985698" + b"5a98649865988598919896989998a59804990699099910991299159918991a99" + b"209921992499269940994299459948994a995199549955995699599962996599" + b"66996a99819984999099929995999a99a199a699059a159a259a449a469a499a" + b"509a559a589a619a859a919a949a959a969a00a002a008a00aa015a020a022a0" + b"28a02aa045a051a054a056a059a080a082a088a08aa095a0a0a0a2a0a8a0aaa0" + b"05a109a111a114a116a119a11aa146a149a151a155a158a15aa161a164a185a1" + b"90a192a196a199a102a208a20aa210a219a222a228a22aa245a251a256a259a2" + b"65a280a282a288a28aa295a2a0a2a2a2a8a2aaa219a425a441a444a450a454a4" + b"55a458a45aa461a465a466a468a469a485a406a509a510a512a515a518a526a5" + b"29a542a545a551a554a555a556a559a565a56aa581a584a585a586a589a592a5" + b"95a598a505a611a616a61aa621a625a644a646a64aa652a655a656a658a660a6" + b"62a686a690a695a696a699a6a1a6a4a6a6a600a802a808a80aa820a822a828a8" + b"2aa851a854a856a859a880a882a888a88aa895a8a0a8a2a8a8a8aaa805a914a9" + b"19a921a925a941a950a955a95aa961a966a969a990a996a900aa02aa08aa0aaa" + b"20aa22aa28aa2aaa51aa54aa56aa80aa82aa88aa8aaa95aaa0aaa2aaa8aaaaaa" + ) + + delta = np.float32(0.125) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + qs, qh = np.hsplit(rest, [QK_K // 8]) + + d = d.view(np.float16).astype(np.float32) + qh = qh.view(np.uint16) + + dl = d * (2 * ((qh >> 12) & 7) + 1) + dl = dl.reshape((n_blocks, -1, 1, 1)) + delta = np.where((qh & np.uint16(0x8000)) == 0, cls.delta, -cls.delta) + delta = delta.reshape((n_blocks, -1, 1, 1)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4)) + qs = qs.astype(np.uint16) | ((qh & 7) << 8).reshape((n_blocks, -1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 4, 8)) + + return (dl * (grid + delta)).reshape((n_blocks, -1)) + + +class IQ1_M(__Quant, qtype=GGMLQuantizationType.IQ1_M): + grid_shape = IQ1_S.grid_shape + grid_map = IQ1_S.grid_map + grid_hex = IQ1_S.grid_hex + + delta = IQ1_S.delta + + # Okay *this* type is weird. It's the only one which stores the f16 scales in multiple parts. + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + qs, rest = np.hsplit(blocks, [QK_K // 8]) + qh, scales = np.hsplit(rest, [QK_K // 16]) + + # The f16 scale is packed across multiple bytes + scales = scales.view(np.uint16) + d = (scales.reshape((n_blocks, 4)) & np.uint16(0xF000)) >> np.array([12, 8, 4, 0], dtype=np.uint16).reshape((1, 4)) + d = d[..., 0] | d[..., 1] | d[..., 2] | d[..., 3] + d = d.view(np.float16).astype(np.float32).reshape((n_blocks, 1)) + + scales = scales.reshape(n_blocks, -1, 1) >> np.array([0, 3, 6, 9], dtype=np.uint16).reshape((1, 1, 4)) + scales = (scales & 0x07).reshape((n_blocks, -1)) + dl = d * (2 * scales + 1) + dl = dl.reshape((n_blocks, -1, 2, 1, 1)) + + qh = qh.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + qs = qs.astype(np.uint16) | ((qh & 0x07).astype(np.uint16) << 8).reshape((n_blocks, -1)) + + delta = np.where(qh & 0x08 == 0, cls.delta, -cls.delta) + delta = delta.reshape((n_blocks, -1, 2, 2, 1)) + + assert cls.grid is not None + grid = np.take_along_axis(cls.grid, qs.reshape((n_blocks, -1, 1, 1)), axis=-2) + grid = grid.reshape((n_blocks, -1, 2, 2, 8)) + + return (dl * (grid + delta)).reshape((n_blocks, -1)) + + +class IQ4_NL(__Quant, qtype=GGMLQuantizationType.IQ4_NL): + kvalues = (-127, -104, -83, -65, -49, -35, -22, -10, 1, 13, 25, 38, 53, 69, 89, 113) + + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, qs = np.hsplit(blocks, [2]) + + d = d.view(np.float16).astype(np.float32) + + qs = qs.reshape((n_blocks, -1, 1, cls.block_size // 2)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + + qs = (qs & np.uint8(0x0F)).reshape((n_blocks, -1, 1)) + + kvalues = np.array(cls.kvalues, dtype=np.int8).reshape(1, 1, 16) + qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1)) + + return (d * qs) + + +class IQ4_XS(__Quant, qtype=GGMLQuantizationType.IQ4_XS): + @classmethod + def dequantize_blocks(cls, blocks: np.ndarray) -> np.ndarray: + n_blocks = blocks.shape[0] + + d, rest = np.hsplit(blocks, [2]) + scales_h, rest = np.hsplit(rest, [2]) + scales_l, qs = np.hsplit(rest, [QK_K // 64]) + + d = d.view(np.float16).astype(np.float32) + scales_h = scales_h.view(np.uint16) + + scales_l = scales_l.reshape((n_blocks, -1, 1)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2)) + scales_h = scales_h.reshape((n_blocks, 1, -1)) >> np.array([2 * i for i in range(QK_K // 32)], dtype=np.uint16).reshape((1, -1, 1)) + scales_l = scales_l.reshape((n_blocks, -1)) & np.uint8(0x0F) + scales_h = scales_h.reshape((n_blocks, -1)).astype(np.uint8) & np.uint8(0x03) + + scales = (scales_l | (scales_h << np.uint8(4))).astype(np.int8) - np.int8(32) + dl = (d * scales.astype(np.float32)).reshape((n_blocks, -1, 1)) + + qs = qs.reshape((n_blocks, -1, 1, 16)) >> np.array([0, 4], dtype=np.uint8).reshape((1, 1, 2, 1)) + qs = qs.reshape((n_blocks, -1, 32, 1)) & np.uint8(0x0F) + + kvalues = np.array(IQ4_NL.kvalues, dtype=np.int8).reshape((1, 1, 1, -1)) + qs = np.take_along_axis(kvalues, qs, axis=-1).astype(np.float32).reshape((n_blocks, -1, 32)) + + return (dl * qs).reshape((n_blocks, -1)) diff --git a/vllm/lib/python3.10/site-packages/gguf/utility.py b/vllm/lib/python3.10/site-packages/gguf/utility.py new file mode 100644 index 0000000000000000000000000000000000000000..40d59b75ee04ec6b46d219ea3be0b3a8fb8b3f35 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/utility.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +from typing import Literal + + +def fill_templated_filename(filename: str, output_type: str | None) -> str: + # Given a file name fill in any type templates e.g. 'some-model-name.{ftype}.gguf' + ftype_lowercase: str = output_type.lower() if output_type is not None else "" + ftype_uppercase: str = output_type.upper() if output_type is not None else "" + return filename.format(ftype_lowercase, + outtype=ftype_lowercase, ftype=ftype_lowercase, + OUTTYPE=ftype_uppercase, FTYPE=ftype_uppercase) + + +def model_weight_count_rounded_notation(model_params_count: int, min_digits: int = 2) -> str: + if model_params_count > 1e12 : + # Trillions Of Parameters + scaled_model_params = model_params_count * 1e-12 + scale_suffix = "T" + elif model_params_count > 1e9 : + # Billions Of Parameters + scaled_model_params = model_params_count * 1e-9 + scale_suffix = "B" + elif model_params_count > 1e6 : + # Millions Of Parameters + scaled_model_params = model_params_count * 1e-6 + scale_suffix = "M" + else: + # Thousands Of Parameters + scaled_model_params = model_params_count * 1e-3 + scale_suffix = "K" + + fix = max(min_digits - len(str(round(scaled_model_params)).lstrip('0')), 0) + + return f"{scaled_model_params:.{fix}f}{scale_suffix}" + + +def size_label(total_params: int, shared_params: int, expert_params: int, expert_count: int) -> str: + + if expert_count > 0: + pretty_size = model_weight_count_rounded_notation(abs(shared_params) + abs(expert_params), min_digits=2) + size_class = f"{expert_count}x{pretty_size}" + else: + size_class = model_weight_count_rounded_notation(abs(total_params), min_digits=2) + + return size_class + + +def naming_convention(model_name: str | None, base_name: str | None, finetune_string: str | None, version_string: str | None, size_label: str | None, output_type: str | None, model_type: Literal['vocab', 'LoRA'] | None = None) -> str: + # Reference: https://github.com/ggerganov/ggml/blob/master/docs/gguf.md#gguf-naming-convention + + if base_name is not None: + name = base_name.strip().replace(' ', '-').replace('/', '-') + elif model_name is not None: + name = model_name.strip().replace(' ', '-').replace('/', '-') + else: + name = "ggml-model" + + parameters = f"-{size_label}" if size_label is not None else "" + + finetune = f"-{finetune_string.strip().replace(' ', '-')}" if finetune_string is not None else "" + + version = f"-{version_string.strip().replace(' ', '-')}" if version_string is not None else "" + + encoding = f"-{output_type.strip().replace(' ', '-').upper()}" if output_type is not None else "" + + kind = f"-{model_type.strip().replace(' ', '-')}" if model_type is not None else "" + + return f"{name}{parameters}{finetune}{version}{encoding}{kind}" diff --git a/vllm/lib/python3.10/site-packages/gguf/vocab.py b/vllm/lib/python3.10/site-packages/gguf/vocab.py new file mode 100644 index 0000000000000000000000000000000000000000..dc574991381a8a9558611cb729c4fb763dd4c444 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/gguf/vocab.py @@ -0,0 +1,465 @@ +from __future__ import annotations + +import re +import logging +import json +import os +from pathlib import Path +from typing import Any, Callable, Sequence, Mapping, Iterable, Protocol, ClassVar, runtime_checkable + +from sentencepiece import SentencePieceProcessor + +import gguf + +from .gguf_writer import GGUFWriter + +logger = logging.getLogger(__name__) + + +class SpecialVocab: + merges: list[str] + add_special_token: dict[str, bool] + special_token_ids: dict[str, int] + chat_template: str | Sequence[Mapping[str, str]] | None + + def __init__( + self, path: str | os.PathLike[str], load_merges: bool = False, + special_token_types: Iterable[str] | None = None, + n_vocab: int | None = None, + ): + self.special_token_ids = {} + self.add_special_token = {} + self.n_vocab = n_vocab + self.load_merges = load_merges + self.merges = [] + self.chat_template = None + if special_token_types is not None: + self.special_token_types = special_token_types + else: + self.special_token_types = ('bos', 'eos', 'unk', 'sep', 'pad', 'cls', 'mask') + self._load(Path(path)) + + def __repr__(self) -> str: + return ''.format( + len(self.merges), self.special_token_ids or "unset", self.add_special_token or "unset", + ) + + def add_to_gguf(self, gw: GGUFWriter, quiet: bool = False) -> None: + if self.merges: + if not quiet: + logger.info(f'Adding {len(self.merges)} merge(s).') + gw.add_token_merges(self.merges) + elif self.load_merges: + logger.warning('Adding merges requested but no merges found, output may be non-functional.') + for typ, tokid in self.special_token_ids.items(): + id_handler: Callable[[int], None] | None = getattr(gw, f'add_{typ}_token_id', None) + if id_handler is None: + logger.warning(f'No handler for special token type {typ} with id {tokid} - skipping') + continue + if not quiet: + logger.info(f'Setting special token type {typ} to {tokid}') + id_handler(tokid) + for typ, value in self.add_special_token.items(): + add_handler: Callable[[bool], None] | None = getattr(gw, f'add_add_{typ}_token', None) + if add_handler is None: + logger.warning(f'No handler for add_{typ}_token with value {value} - skipping') + continue + if not quiet: + logger.info(f'Setting add_{typ}_token to {value}') + add_handler(value) + if self.chat_template is not None: + if not quiet: + logger.info(f'Setting chat_template to {self.chat_template}') + gw.add_chat_template(self.chat_template) + + def _load(self, path: Path) -> None: + self._try_load_from_tokenizer_json(path) + self._try_load_from_config_json(path) + if self.load_merges and not self.merges: + self._try_load_merges_txt(path) + + def _try_load_merges_txt(self, path: Path) -> bool: + merges_file = path / 'merges.txt' + if not merges_file.is_file(): + return False + with open(merges_file, 'r', encoding = 'utf-8') as fp: + first_line = next(fp, '').strip() + if not first_line.startswith('#'): + fp.seek(0) + line_num = 0 + else: + line_num = 1 + merges = [] + for line in fp: + line_num += 1 + line = line.strip() + if not line: + continue + parts = line.split(None, 3) + if len(parts) != 2: + logger.warning(f'{merges_file.name}: Line {line_num}: Entry malformed, ignoring') + continue + merges.append(f'{parts[0]} {parts[1]}') + self.merges = merges + return True + + def _set_special_token(self, typ: str, tid: Any) -> None: + if not isinstance(tid, int): + return + if tid < 0: + raise ValueError(f'invalid value for special token type {typ}: {tid}') + if self.n_vocab is None or tid < self.n_vocab: + if typ in self.special_token_ids: + return + self.special_token_ids[typ] = tid + return + logger.warning(f'Special token type {typ}, id {tid} out of range, must be under {self.n_vocab} - skipping') + + def _try_load_from_tokenizer_json(self, path: Path) -> bool: + tokenizer_file = path / 'tokenizer.json' + if tokenizer_file.is_file(): + with open(tokenizer_file, encoding = 'utf-8') as f: + tokenizer = json.load(f) + if self.load_merges: + merges = tokenizer.get('model', {}).get('merges') + if isinstance(merges, list) and merges and isinstance(merges[0], str): + self.merges = merges + added_tokens = tokenizer.get('added_tokens', {}) + else: + added_tokens = {} + tokenizer_config_file = path / 'tokenizer_config.json' + if not tokenizer_config_file.is_file(): + return True + with open(tokenizer_config_file, encoding = 'utf-8') as f: + tokenizer_config = json.load(f) + chat_template = tokenizer_config.get('chat_template') + if chat_template is None or isinstance(chat_template, (str, list)): + self.chat_template = chat_template + else: + logger.warning(f'Bad type for chat_template field in {tokenizer_config_file!r} - ignoring') + for typ in self.special_token_types: + add_entry = tokenizer_config.get(f'add_{typ}_token') + if isinstance(add_entry, bool): + self.add_special_token[typ] = add_entry + entry = tokenizer_config.get(f'{typ}_token') + if isinstance(entry, str): + tc_content = entry + elif isinstance(entry, dict): + entry_content = entry.get('content') + if not isinstance(entry_content, str): + continue + tc_content = entry_content + else: + continue + # We only need the first match here. + maybe_token_id = next( + (atok.get('id') for atok in added_tokens if atok.get('content') == tc_content), + None, + ) + self._set_special_token(typ, maybe_token_id) + return True + + def _try_load_from_config_json(self, path: Path) -> bool: + config_file = path / 'config.json' + if not config_file.is_file(): + return False + with open(config_file, encoding = 'utf-8') as f: + config = json.load(f) + for typ in self.special_token_types: + self._set_special_token(typ, config.get(f'{typ}_token_id')) + return True + + +@runtime_checkable +class BaseVocab(Protocol): + tokenizer_model: ClassVar[str] + name: ClassVar[str] + + +@runtime_checkable +class Vocab(BaseVocab, Protocol): + vocab_size: int + added_tokens_dict: dict[str, int] + added_tokens_list: list[str] + fname_tokenizer: Path + + def __init__(self, base_path: Path): ... + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: ... + + +class NoVocab(BaseVocab): + tokenizer_model = "no_vocab" + name = "no_vocab" + + def __repr__(self) -> str: + return "" + + +class BpeVocab(Vocab): + tokenizer_model = "gpt2" + name = "bpe" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + + if (fname_tokenizer := base_path / 'vocab.json').exists(): + # "slow" tokenizer + with open(fname_tokenizer, encoding="utf-8") as f: + self.vocab = json.load(f) + + try: + # FIXME: Verify that added tokens here _cannot_ overlap with the main vocab. + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + else: + # "fast" tokenizer + fname_tokenizer = base_path / 'tokenizer.json' + + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding="utf-8") as f: + tokenizer_json = json.load(f) + + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + if ( + tokenizer_model['type'] != 'BPE' or tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'ByteLevel' + ): + raise FileNotFoundError('Cannot find GPT-2 BPE tokenizer') + + self.vocab = tokenizer_model["vocab"] + + if (added := tokenizer_json.get('added_tokens')) is not None: + # Added tokens here can be duplicates of the main vocabulary. + added_tokens = {item['content']: item['id'] + for item in added + if item['content'] not in self.vocab} + + vocab_size = len(self.vocab) + expected_ids = list(range(vocab_size, vocab_size + len(added_tokens))) + actual_ids = sorted(added_tokens.values()) + if expected_ids != actual_ids: + expected_end_id = vocab_size + len(actual_ids) - 1 + raise ValueError(f"Expected the {len(actual_ids)} added token ID(s) to be sequential in the range " + f"{vocab_size} - {expected_end_id}; got {actual_ids}") + + items = sorted(added_tokens.items(), key=lambda text_idx: text_idx[1]) + self.added_tokens_dict = added_tokens + self.added_tokens_list = [text for (text, idx) in items] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def bpe_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = {id: encoded_tok for encoded_tok, id in self.vocab.items()} + + for i, _ in enumerate(self.vocab): + yield reverse_vocab[i], 0.0, gguf.TokenType.NORMAL + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.CONTROL + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.bpe_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class SentencePieceVocab(Vocab): + tokenizer_model = "llama" + name = "spm" + + def __init__(self, base_path: Path): + added_tokens: dict[str, int] = {} + if (fname_tokenizer := base_path / 'tokenizer.model').exists(): + # normal location + try: + with open(base_path / 'added_tokens.json', encoding="utf-8") as f: + added_tokens = json.load(f) + except FileNotFoundError: + pass + elif not (fname_tokenizer := base_path.parent / 'tokenizer.model').exists(): + # not found in alternate location either + raise FileNotFoundError('Cannot find tokenizer.model') + + self.sentencepiece_tokenizer = SentencePieceProcessor() + self.sentencepiece_tokenizer.LoadFromFile(str(fname_tokenizer)) + vocab_size = self.sentencepiece_tokenizer.vocab_size() + + new_tokens = {id: piece for piece, id in added_tokens.items() if id >= vocab_size} + expected_new_ids = list(range(vocab_size, vocab_size + len(new_tokens))) + actual_new_ids = sorted(new_tokens.keys()) + + if expected_new_ids != actual_new_ids: + raise ValueError(f"Expected new token IDs {expected_new_ids} to be sequential; got {actual_new_ids}") + + # Token pieces that were added to the base vocabulary. + self.added_tokens_dict = added_tokens + self.added_tokens_list = [new_tokens[id] for id in actual_new_ids] + self.vocab_size_base = vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + self.fname_tokenizer = fname_tokenizer + + def sentencepiece_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + tokenizer = self.sentencepiece_tokenizer + for i in range(tokenizer.vocab_size()): + piece = tokenizer.IdToPiece(i) + text = piece.encode("utf-8") + score: float = tokenizer.GetScore(i) + + toktype = gguf.TokenType.NORMAL + if tokenizer.IsUnknown(i): + toktype = gguf.TokenType.UNKNOWN + if tokenizer.IsControl(i): + toktype = gguf.TokenType.CONTROL + + # NOTE: I think added_tokens are user defined. + # ref: https://github.com/google/sentencepiece/blob/master/src/sentencepiece_model.proto + # if tokenizer.is_user_defined(i): toktype = gguf.TokenType.USER_DEFINED + + if tokenizer.IsUnused(i): + toktype = gguf.TokenType.UNUSED + if tokenizer.IsByte(i): + toktype = gguf.TokenType.BYTE + + yield text, score, toktype + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + score = -1000.0 + yield text.encode("utf-8"), score, gguf.TokenType.USER_DEFINED + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.sentencepiece_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" + + +class LlamaHfVocab(Vocab): + tokenizer_model = "llama" + name = "hfft" + + def __init__(self, base_path: Path): + fname_tokenizer = base_path / 'tokenizer.json' + # if this fails, FileNotFoundError propagates to caller + with open(fname_tokenizer, encoding='utf-8') as f: + tokenizer_json = json.load(f) + + # pre-check so we know if we need transformers + tokenizer_model: dict[str, Any] = tokenizer_json['model'] + is_llama3 = ( + tokenizer_model['type'] == 'BPE' and tokenizer_model.get('ignore_merges', False) + and not tokenizer_model.get('byte_fallback', True) + ) + if is_llama3: + raise TypeError('Llama 3 must be converted with BpeVocab') + + if not is_llama3 and ( + tokenizer_model['type'] != 'BPE' or not tokenizer_model.get('byte_fallback', False) + or tokenizer_json['decoder']['type'] != 'Sequence' + ): + raise FileNotFoundError('Cannot find Llama BPE tokenizer') + + try: + from transformers import AutoTokenizer + except ImportError as e: + raise ImportError( + "To use LlamaHfVocab, please install the `transformers` package. " + "You can install it with `pip install transformers`." + ) from e + + # Allow the tokenizer to default to slow or fast versions. + # Explicitly set tokenizer to use local paths. + self.tokenizer = AutoTokenizer.from_pretrained( + base_path, + cache_dir=base_path, + local_files_only=True, + ) + assert self.tokenizer.is_fast # assume tokenizer.json is used + + # Initialize lists and dictionaries for added tokens + self.added_tokens_list = [] + self.added_tokens_dict = dict() + self.added_tokens_ids = set() + + # Process added tokens + for tok, tokidx in sorted( + self.tokenizer.get_added_vocab().items(), key=lambda x: x[1] + ): + # Only consider added tokens that are not in the base vocabulary + if tokidx >= self.tokenizer.vocab_size: + self.added_tokens_list.append(tok) + self.added_tokens_dict[tok] = tokidx + self.added_tokens_ids.add(tokidx) + + # Store special tokens and their IDs + self.specials = { + tok: self.tokenizer.get_vocab()[tok] + for tok in self.tokenizer.all_special_tokens + } + self.special_ids = set(self.tokenizer.all_special_ids) + + # Set vocabulary sizes + self.vocab_size_base = self.tokenizer.vocab_size + self.vocab_size = self.vocab_size_base + len(self.added_tokens_list) + + self.fname_tokenizer = fname_tokenizer + + def hf_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + reverse_vocab = { + id: encoded_tok for encoded_tok, id in self.tokenizer.get_vocab().items() + } + + for token_id in range(self.vocab_size_base): + # Skip processing added tokens here + if token_id in self.added_tokens_ids: + continue + + # Convert token text to bytes + token_text = reverse_vocab[token_id].encode("utf-8") + + # Yield token text, score, and type + yield token_text, self.get_token_score(token_id), self.get_token_type( + token_id, token_text, self.special_ids # Reuse already stored special IDs + ) + + def get_token_type(self, token_id: int, token_text: bytes, special_ids: set[int]) -> gguf.TokenType: + # Special case for byte tokens + if re.fullmatch(br"<0x[0-9A-Fa-f]{2}>", token_text): + return gguf.TokenType.BYTE + + # Determine token type based on whether it's a special token + return gguf.TokenType.CONTROL if token_id in special_ids else gguf.TokenType.NORMAL + + def get_token_score(self, token_id: int) -> float: + # Placeholder for actual logic to determine the token's score + # This needs to be implemented based on specific requirements + return -1000.0 # Default score + + def added_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + for text in self.added_tokens_list: + if text in self.specials: + toktype = self.get_token_type(self.specials[text], b'', self.special_ids) + score = self.get_token_score(self.specials[text]) + else: + toktype = gguf.TokenType.USER_DEFINED + score = -1000.0 + + yield text.encode("utf-8"), score, toktype + + def has_newline_token(self): + return "<0x0A>" in self.tokenizer.vocab or "\n" in self.tokenizer.vocab + + def all_tokens(self) -> Iterable[tuple[bytes, float, gguf.TokenType]]: + yield from self.hf_tokens() + yield from self.added_tokens() + + def __repr__(self) -> str: + return f"" diff --git a/vllm/lib/python3.10/site-packages/outlines/_version.py b/vllm/lib/python3.10/site-packages/outlines/_version.py new file mode 100644 index 0000000000000000000000000000000000000000..72c1ac0e4780433a3be4a9bc80c43962a048f6e5 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/outlines/_version.py @@ -0,0 +1,16 @@ +# file generated by setuptools_scm +# don't change, don't track in version control +TYPE_CHECKING = False +if TYPE_CHECKING: + from typing import Tuple, Union + VERSION_TUPLE = Tuple[Union[int, str], ...] +else: + VERSION_TUPLE = object + +version: str +__version__: str +__version_tuple__: VERSION_TUPLE +version_tuple: VERSION_TUPLE + +__version__ = version = '0.1.11' +__version_tuple__ = version_tuple = (0, 1, 11) diff --git a/vllm/lib/python3.10/site-packages/outlines/base.py b/vllm/lib/python3.10/site-packages/outlines/base.py new file mode 100644 index 0000000000000000000000000000000000000000..29d42c54c2835570cef97b94520bc06e69fcc80c --- /dev/null +++ b/vllm/lib/python3.10/site-packages/outlines/base.py @@ -0,0 +1,299 @@ +import asyncio +import builtins +import functools +import inspect +from typing import Callable, Optional + +import numpy as np + +# Import required functions based on NumPy version +np_major_version = int(np.__version__.split(".")[0]) +if np_major_version >= 2: + from numpy.lib._function_base_impl import ( + _calculate_shapes, + _parse_gufunc_signature, + _parse_input_dimensions, + _update_dim_sizes, + ) +else: + from numpy.lib.function_base import ( + _calculate_shapes, + _parse_gufunc_signature, + _parse_input_dimensions, + _update_dim_sizes, + ) + +# Allow nested loops for running in notebook. We don't enable it globally as it +# may interfere with other libraries that use asyncio. +if hasattr(builtins, "__IPYTHON__"): + try: + import nest_asyncio + + nest_asyncio.apply() + except ImportError: + print( + "Couldn't patch nest_asyncio because it's not installed. Running in the notebook might be have issues" + ) + + +class vectorize: + """Returns an object that acts like a function but takes arrays as an input. + + The vectorized function evaluates `func` over successive tuples of the input + chararrays and returns a single NumPy chararrays or a tuple of NumPy chararrays. + + Its behavior is similar to NumPy's `vectorize` for Python functions: the function + being vectorized is executed in a `for` loop. Coroutines, however, are executed + concurrently. + + Part of the code was adapted from `numpy.lib.function_base`. + + """ + + def __init__(self, func: Callable, signature: Optional[str] = None): + self.func = func + self.signature = signature + self.is_coroutine_fn = inspect.iscoroutinefunction(func) + + functools.update_wrapper(self, func) + + if signature is not None: + # Parse the signature string into a Python data structure. + # For instance "(m),(s)->(s,m)" becomes `([(m,),(s,)],[(s,m)])`. + self._in_and_out_core_dimensions = _parse_gufunc_signature(signature) + else: + self._in_and_out_core_dimensions = None + + def __call__(self, *args, **kwargs): + """Call the vectorized function.""" + if not args and not kwargs: + return self.call_thunk() + elif self.signature is not None: + return self.call_with_signature(*args, **kwargs) + else: + return self.call_no_signature(*args, **kwargs) + + def call_thunk(self): + """Call a vectorized thunk. + + Thunks have no arguments and can thus be called directly. + + """ + if self.is_coroutine_fn: + loop = asyncio.new_event_loop() + try: + outputs = loop.run_until_complete(self.func()) + finally: + loop.close() + else: + outputs = self.func() + + return outputs + + def call_no_signature(self, *args, **kwargs): + """Call functions and coroutines when no signature is specified. + + When no signature is specified we assume that all of the function's + inputs and outputs are scalars (core dimension of zero). We first + broadcast the input arrays, then iteratively apply the function over the + elements of the broadcasted arrays and finally reshape the results to + match the input shape. + + Functions are executed in a for loop, coroutines are executed + concurrently. + + """ + # Convert args and kwargs to arrays + args = [np.array(arg) for arg in args] + kwargs = {key: np.array(value) for key, value in kwargs.items()} + + # Broadcast args and kwargs + broadcast_shape = np.broadcast(*args, *list(kwargs.values())).shape + args = [np.broadcast_to(arg, broadcast_shape) for arg in args] + kwargs = { + key: np.broadcast_to(value, broadcast_shape) + for key, value in kwargs.items() + } + + # Execute functions in a loop, and coroutines concurrently + if self.is_coroutine_fn: + outputs = self.vectorize_call_coroutine(broadcast_shape, args, kwargs) + else: + outputs = self.vectorize_call(broadcast_shape, args, kwargs) + + # `outputs` is a flat array or a tuple of flat arrays. We reshape the arrays + # to match the input shape. + outputs = [ + results if isinstance(results, tuple) else (results,) for results in outputs + ] + outputs = tuple( + [np.asarray(x).reshape(broadcast_shape).squeeze() for x in zip(*outputs)] + ) + outputs = tuple([x.item() if np.ndim(x) == 0 else x for x in outputs]) + + n_results = len(list(outputs)) + + return outputs[0] if n_results == 1 else outputs + + def call_with_signature(self, *args, **kwargs): + """Call functions and coroutines when a signature is specified.""" + input_core_dims, output_core_dims = self._in_and_out_core_dimensions + + # Make sure that the numbers of arguments passed is compatible with + # the signature. + num_args = len(args) + len(kwargs) + if num_args != len(input_core_dims): + raise TypeError( + "wrong number of positional arguments: " + "expected %r, got %r" % (len(input_core_dims), len(args)) + ) + + # Convert args and kwargs to arrays + args = [np.asarray(arg) for arg in args] + kwargs = {key: np.array(value) for key, value in kwargs.items()} + + # Find the arguments' broadcast shape, and map placeholder + # variables in the signature to the number of dimensions + # they correspond to given the arguments. + broadcast_shape, dim_sizes = _parse_input_dimensions( + args + list(kwargs.values()), input_core_dims + ) + + # Calculate the shape to which each of the arguments should be broadcasted + # and reshape them accordingly. + input_shapes = _calculate_shapes(broadcast_shape, dim_sizes, input_core_dims) + args = [ + np.broadcast_to(arg, shape, subok=True) + for arg, shape in zip(args, input_shapes) + ] + kwargs = { + key: np.broadcast_to(value, broadcast_shape) + for key, value in kwargs.items() + } + + n_out = len(output_core_dims) + + if self.is_coroutine_fn: + outputs = self.vectorize_call_coroutine(broadcast_shape, args, kwargs) + else: + outputs = self.vectorize_call(broadcast_shape, args, kwargs) + + outputs = [ + results if isinstance(results, tuple) else (results,) for results in outputs + ] + + flat_outputs = list(zip(*outputs)) + n_results = len(flat_outputs) + + if n_out != n_results: + raise ValueError( + f"wrong number of outputs from the function, expected {n_out}, got {n_results}" + ) + + # The number of dimensions of the outputs are not necessarily known in + # advance. The following iterates over the results and updates the + # number of dimensions of the outputs accordingly. + for results, core_dims in zip(flat_outputs, output_core_dims): + for result in results: + _update_dim_sizes(dim_sizes, result, core_dims) + + # Calculate the shape to which each of the outputs should be broadcasted + # and reshape them. + shapes = _calculate_shapes(broadcast_shape, dim_sizes, output_core_dims) + outputs = tuple( + [ + np.hstack(results).reshape(shape).squeeze() + for shape, results in zip(shapes, zip(*outputs)) + ] + ) + outputs = tuple([x.item() if np.ndim(x) == 0 else x for x in outputs]) + + return outputs[0] if n_results == 1 else outputs + + def vectorize_call(self, broadcast_shape, args, kwargs): + """Run the function in a for loop. + + A possible extension would be to parallelize the calls. + + Parameters + ---------- + broadcast_shape + The brodcast shape of the input arrays. + args + The function's broadcasted arguments. + kwargs + The function's broadcasted keyword arguments. + + """ + outputs = [] + for index in np.ndindex(*broadcast_shape): + current_args = tuple(arg[index] for arg in args) + current_kwargs = {key: value[index] for key, value in kwargs.items()} + outputs.append(self.func(*current_args, **current_kwargs)) + + return outputs + + def vectorize_call_coroutine(self, broadcast_shape, args, kwargs): + """Run coroutines concurrently. + + Creates as many tasks as needed and executes them in a new event + loop. + + Parameters + ---------- + broadcast_shape + The brodcast shape of the input arrays. + args + The function's broadcasted arguments. + kwargs + The function's broadcasted keyword arguments. + + """ + + async def create_and_gather_tasks(): + tasks = [] + for index in np.ndindex(*broadcast_shape): + current_args = tuple(arg[index] for arg in args) + current_kwargs = {key: value[index] for key, value in kwargs.items()} + tasks.append(self.func(*current_args, **current_kwargs)) + + outputs = await asyncio.gather(*tasks) + + return outputs + + loop = asyncio.new_event_loop() + try: + outputs = loop.run_until_complete(create_and_gather_tasks()) + finally: + loop.close() + + return outputs + + +def _update_arrays_type(arrays, results): + """Update the dtype of arrays. + + String arrays contain strings of fixed length. Here they are initialized with + the type of the first results, so that if the next results contain longer + strings they will be truncated when added to the output arrays. Here we + update the type if the current results contain longer strings than in the + current output array. + + Parameters + ---------- + arrays + Arrays that contain the vectorized function's results. + results + The current output of the function being vectorized. + + """ + + updated_arrays = [] + for array, result in zip(arrays, results): + if array.dtype.type == np.str_: + if array.dtype < np.array(result).dtype: + array = array.astype(np.array(result).dtype) + + updated_arrays.append(array) + + return tuple(updated_arrays) diff --git a/vllm/lib/python3.10/site-packages/outlines/prompts.py b/vllm/lib/python3.10/site-packages/outlines/prompts.py new file mode 100644 index 0000000000000000000000000000000000000000..a7824451a19b9a60cbda59455ea0e3900526dd44 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/outlines/prompts.py @@ -0,0 +1,343 @@ +import functools +import inspect +import json +import re +import textwrap +from dataclasses import dataclass +from typing import Any, Callable, Dict, List, Optional, Type, cast + +from jinja2 import Environment, StrictUndefined +from pydantic import BaseModel + + +@dataclass +class Prompt: + """Represents a prompt function. + + We return a `Prompt` class instead of a simple function so the + template defined in prompt functions can be accessed. + + """ + + template: str + signature: inspect.Signature + + def __post_init__(self): + self.parameters: List[str] = list(self.signature.parameters.keys()) + self.jinja_environment = create_jinja_template(self.template) + + def __call__(self, *args, **kwargs) -> str: + """Render and return the template. + + Returns + ------- + The rendered template as a Python ``str``. + + """ + bound_arguments = self.signature.bind(*args, **kwargs) + bound_arguments.apply_defaults() + return self.jinja_environment.render(**bound_arguments.arguments) + + def __str__(self): + return self.template + + +def prompt(fn: Callable) -> Prompt: + """Decorate a function that contains a prompt template. + + This allows to define prompts in the docstring of a function and simplify their + manipulation by providing some degree of encapsulation. It uses the `render` + function internally to render templates. + + >>> import outlines + >>> + >>> @outlines.prompt + >>> def build_prompt(question): + ... "I have a ${question}" + ... + >>> prompt = build_prompt("How are you?") + + This API can also be helpful in an "agent" context where parts of the prompt + are set when the agent is initialized and never modified later. In this situation + we can partially apply the prompt function at initialization. + + >>> import outlines + >>> import functools as ft + ... + >>> @outlines.prompt + ... def solve_task(name: str, objective: str, task: str): + ... '''Your name is {{name}}. + .. Your overall objective is to {{objective}}. + ... Please solve the following task: {{task}} + ... ''' + ... + >>> hal = ft.partial(solve_task, "HAL", "Travel to Jupiter") + + Returns + ------- + A `Prompt` callable class which will render the template when called. + + """ + + signature = inspect.signature(fn) + + # The docstring contains the template that will be rendered to be used + # as a prompt to the language model. + docstring = fn.__doc__ + if docstring is None: + raise TypeError("Could not find a template in the function's docstring.") + + template = cast(str, docstring) + + return Prompt(template, signature) + + +def render(template: str, **values: Optional[Dict[str, Any]]) -> str: + r"""Parse a Jinaj2 template and translate it into an Outlines graph. + + This function removes extra whitespaces and linebreaks from templates to + allow users to enter prompts more naturally than if they used Python's + constructs directly. See the examples for a detailed explanation. + + Examples + -------- + + Outlines follow Jinja2's syntax + + >>> import outlines + >>> outline = outlines.render("I like {{food}} and {{sport}}", food="tomatoes", sport="tennis") + I like tomatoes and tennis + + If the first line of the template is empty, `render` removes it + + >>> from outlines import render + >>> + >>> tpl = ''' + ... A new string''' + >>> tpl + ... '\nA new string' + >>> render(tpl) + ... 'a new string' + + Similarly, `render` ignores linebreaks introduced by placing the closing quotes + underneath the text: + + >>> tpl = ''' + ... A new string + ... ''' + >>> tpl + ... '\nA new string\n' + >>> render(tpl) + ... 'A new string' + + If you want to insert a linebreak at the end of the rendered template, you will + need to leave an empty line at the end of the template: + + >>> tpl = ''' + ... A new string + ... + ... ''' + >>> tpl + ... '\nA new string\n\n' + >>> render(tpl) + ... 'A new string\n' + + `render` removes the identation in docstrings. This is particularly important + when using prompt functions + + >>> tpl = ''' + ... a string + ... and another string''' + >>> tpl + ... '\n a string\n and another string' + >>> render(tpl) + ... 'a string\nand another string' + + The indentation of the first line is assumed to be the same as the second line's + + >>> tpl = '''a string + ... and another''' + >>> tpl + ... 'a string\n and another' + >>> render(tpl) + ... 'a string\nand another' + + To get a different indentation for the first and the second line, we can start the + prompt on the string's second line: + + >>> tpl = ''' + ... First line + ... Second line''' + >>> render(tpl) + ... 'First Line\n Second Line' + + Parameters + ---------- + template + A string that contains a template written with the Jinja2 syntax. + **values + Map from the variables in the template to their value. + + Returns + ------- + A string that contains the rendered template. + + """ + jinja_template = create_jinja_template(template) + return jinja_template.render(**values) + + +def create_jinja_template(template: str): + # Dedent, and remove extra linebreak + cleaned_template = inspect.cleandoc(template) + + # Add linebreak if there were any extra linebreaks that + # `cleandoc` would have removed + ends_with_linebreak = template.replace(" ", "").endswith("\n\n") + if ends_with_linebreak: + cleaned_template += "\n" + + # Remove extra whitespaces, except those that immediately follow a newline symbol. + # This is necessary to avoid introducing whitespaces after backslash `\` characters + # used to continue to the next line without linebreak. + cleaned_template = re.sub(r"(?![\r\n])(\b\s+)", " ", cleaned_template) + + env = Environment( + trim_blocks=True, + lstrip_blocks=True, + keep_trailing_newline=True, + undefined=StrictUndefined, + ) + env.filters["name"] = get_fn_name + env.filters["description"] = get_fn_description + env.filters["source"] = get_fn_source + env.filters["signature"] = get_fn_signature + env.filters["schema"] = get_schema + env.filters["args"] = get_fn_args + + jinja_template = env.from_string(cleaned_template) + return jinja_template + + +def get_fn_name(fn: Callable): + """Returns the name of a callable.""" + if not callable(fn): + raise TypeError("The `name` filter only applies to callables.") + + if not hasattr(fn, "__name__"): + name = type(fn).__name__ + else: + name = fn.__name__ + + return name + + +def get_fn_args(fn: Callable): + """Returns the arguments of a function with annotations and default values if provided.""" + if not callable(fn): + raise TypeError("The `args` filter only applies to callables.") + + arg_str_list = [] + signature = inspect.signature(fn) + arg_str_list = [str(param) for param in signature.parameters.values()] + arg_str = ", ".join(arg_str_list) + return arg_str + + +def get_fn_description(fn: Callable): + """Returns the first line of a callable's docstring.""" + if not callable(fn): + raise TypeError("The `description` filter only applies to callables.") + + docstring = inspect.getdoc(fn) + if docstring is None: + description = "" + else: + description = docstring.split("\n")[0].strip() + + return description + + +def get_fn_source(fn: Callable): + """Return the source code of a callable.""" + if not callable(fn): + raise TypeError("The `source` filter only applies to callables.") + + source = textwrap.dedent(inspect.getsource(fn)) + re_search = re.search(re.compile(r"(\bdef\b.*)", re.DOTALL), source) + if re_search is not None: + source = re_search.group(0) + else: + raise TypeError("Could not read the function's source code") + + return source + + +def get_fn_signature(fn: Callable): + """Return the signature of a callable.""" + if not callable(fn): + raise TypeError("The `source` filter only applies to callables.") + + source = textwrap.dedent(inspect.getsource(fn)) + re_search = re.search(re.compile(r"\(([^)]+)\)"), source) + if re_search is None: + signature = "" + else: + signature = re_search.group(1) + + return signature + + +@functools.singledispatch +def get_schema(model: Any): + raise NotImplementedError( + f"No schema rendering function defined for type {type(model)}." + ) + + +@get_schema.register(dict) +def get_schema_dict(model: Dict): + """Return a pretty-printed dictionary""" + return json.dumps(model, indent=2) + + +@get_schema.register(type(BaseModel)) +def get_schema_pydantic(model: Type[BaseModel]): + """Return the schema of a Pydantic model.""" + if not type(model) == type(BaseModel): + raise TypeError("The `schema` filter only applies to Pydantic models.") + + if hasattr(model, "model_json_schema"): + def_key = "$defs" + raw_schema = model.model_json_schema() + else: # pragma: no cover + def_key = "definitions" + raw_schema = model.schema() + + definitions = raw_schema.get(def_key, None) + schema = parse_pydantic_schema(raw_schema, definitions) + + return json.dumps(schema, indent=2) + + +def parse_pydantic_schema(raw_schema, definitions): + """Parse the output of `Basemodel.[schema|model_json_schema]()`. + + This recursively follows the references to other schemas in case + of nested models. Other schemas are stored under the "definitions" + key in the schema of the top-level model. + + """ + simple_schema = {} + for name, value in raw_schema["properties"].items(): + if "description" in value: + simple_schema[name] = value["description"] + elif "$ref" in value: + refs = value["$ref"].split("/") + simple_schema[name] = parse_pydantic_schema( + definitions[refs[2]], definitions + ) + else: + simple_schema[name] = f"<{name}>" + + return simple_schema diff --git a/vllm/lib/python3.10/site-packages/outlines/py.typed b/vllm/lib/python3.10/site-packages/outlines/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/vllm/lib/python3.10/site-packages/outlines/serve/__pycache__/serve.cpython-310.pyc b/vllm/lib/python3.10/site-packages/outlines/serve/__pycache__/serve.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..c0024d8f14575d058d446ca1457f466cb364352d Binary files /dev/null and b/vllm/lib/python3.10/site-packages/outlines/serve/__pycache__/serve.cpython-310.pyc differ diff --git a/vllm/lib/python3.10/site-packages/outlines/serve/serve.py b/vllm/lib/python3.10/site-packages/outlines/serve/serve.py new file mode 100644 index 0000000000000000000000000000000000000000..998fbc4594752aa2dd6b91222b5bc0d343eefab7 --- /dev/null +++ b/vllm/lib/python3.10/site-packages/outlines/serve/serve.py @@ -0,0 +1,139 @@ +# _______________________________ +# / Don't want to self-host? \ +# \ Try .json at http://dottxt.co / +# ------------------------------- +# \ ^__^ +# \ (oo)\_______ +# (__)\ )\/\ +# ||----w | +# || || +# +# +# Copyright 2024- the Outlines developers +# Copyright 2023 the vLLM developers +# +# 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. +import argparse +import json +from typing import AsyncGenerator + +import uvicorn +from fastapi import FastAPI, Request +from fastapi.responses import JSONResponse, Response, StreamingResponse +from vllm.engine.arg_utils import AsyncEngineArgs +from vllm.engine.async_llm_engine import AsyncLLMEngine +from vllm.sampling_params import SamplingParams +from vllm.utils import random_uuid + +from outlines.models.vllm import adapt_tokenizer +from outlines.processors import JSONLogitsProcessor, RegexLogitsProcessor + +TIMEOUT_KEEP_ALIVE = 5 # seconds. +TIMEOUT_TO_PREVENT_DEADLOCK = 1 # seconds. +app = FastAPI() +engine = None +tokenizer = None + + +@app.get("/health") +async def health() -> Response: + """Health check.""" + return Response(status_code=200) + + +@app.post("/generate") +async def generate(request: Request) -> Response: + """Generate completion for the request. + + The request should be a JSON object with the following fields: + - prompt: the prompt to use for the generation. + - schema: the JSON schema to use for the generation (if regex is not provided). + - regex: the regex to use for the generation (if schema is not provided). + - stream: whether to stream the results or not. + - other fields: the sampling parameters (See `SamplingParams` for details). + """ + assert engine is not None + + request_dict = await request.json() + prompt = request_dict.pop("prompt") + stream = request_dict.pop("stream", False) + + json_schema = request_dict.pop("schema", None) + regex_string = request_dict.pop("regex", None) + if json_schema is not None: + logits_processors = [JSONLogitsProcessor(json_schema, tokenizer)] + elif regex_string is not None: + logits_processors = [RegexLogitsProcessor(regex_string, tokenizer)] + else: + logits_processors = [] + + sampling_params = SamplingParams( + **request_dict, logits_processors=logits_processors # type: ignore + ) + request_id = random_uuid() + + results_generator = engine.generate(prompt, sampling_params, request_id) # type: ignore + + # Streaming case + async def stream_results() -> AsyncGenerator[bytes, None]: + async for request_output in results_generator: + prompt = request_output.prompt + text_outputs = [prompt + output.text for output in request_output.outputs] + ret = {"text": text_outputs} + yield (json.dumps(ret) + "\0").encode("utf-8") + + if stream: + return StreamingResponse(stream_results()) + + # Non-streaming case + final_output = None + async for request_output in results_generator: + if await request.is_disconnected(): + # Abort the request if the client disconnects. + await engine.abort(request_id) # type: ignore + return Response(status_code=499) + final_output = request_output + + assert final_output is not None + prompt = final_output.prompt + text_outputs = [prompt + output.text for output in final_output.outputs] + ret = {"text": text_outputs} + return JSONResponse(ret) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--host", type=str, default=None) + parser.add_argument("--port", type=int, default=8000) + parser.add_argument("--ssl-keyfile", type=str, default=None) + parser.add_argument("--ssl-certfile", type=str, default=None) + parser = AsyncEngineArgs.add_cli_args(parser) + args = parser.parse_args() + + # Adds the `engine_use_ray`, `disable_log_requests` and `max_log_len` + # arguments + engine_args: AsyncEngineArgs = AsyncEngineArgs.from_cli_args(args) # type: ignore + + # Sets default for the model (`facebook/opt-125m`) + engine = AsyncLLMEngine.from_engine_args(engine_args) + tokenizer = adapt_tokenizer(tokenizer=engine.engine.tokenizer.tokenizer) + + uvicorn.run( + app, + host=args.host, + port=args.port, + 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