Apply quantization on megablox kernel; support both training and serving

MaxText/kernels/megablox/__init__.py

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+# Copyright 2024 Google LLC
+#
+# 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
+#
+#     https://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.
+"""Megablox kernel"""
+
+from kernels.megablox.ops import gmm

MaxText/kernels/megablox/common.py

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+# Copyright 2024 Google LLC
+#
+# 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
+#
+#     https://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.
+
+"""Common utilities for GMM kernels."""
+
+import re
+
+import jax
+import jax.numpy as jnp
+
+
+def is_tpu() -> bool:
+  return "TPU" in jax.devices()[0].device_kind
+
+
+def tpu_kind() -> str:
+  """Query identification string for the currently attached TPU."""
+  return jax.devices()[0].device_kind
+
+
+_TPU_KIND_PATTERN = re.compile(r"TPU v(\d+)")
+
+
+def tpu_generation() -> int:
+  """Generation number of the currently attached TPU."""
+  if version := _TPU_KIND_PATTERN.match(tpu_kind()):
+    return int(version[1])
+  raise NotImplementedError("only TPU devices are supported")
+
+
+def supports_bfloat16_matmul() -> bool:
+  """Does the currently attached CPU support bfloat16 inputs?"""
+  return not is_tpu() or tpu_generation() >= 4
+
+
+def assert_is_supported_dtype(dtype: jnp.dtype) -> None:
+  if dtype not in (jnp.bfloat16, jnp.float32):
+    raise ValueError(f"Expected bfloat16 or float32 array but got {dtype}.")
+
+
+def select_input_dtype(lhs: jnp.ndarray, rhs: jnp.ndarray) -> jnp.dtype:
+  """A type to which both input should be adapted to before dot product."""
+  # bf16xbf16 matmul is only supported since TPUv4 generation. In case of mixed
+  # input precision, we need to convert bf16 argument to fp32 beforehand.
+  if supports_bfloat16_matmul() and lhs.dtype == jnp.bfloat16 and rhs.dtype == jnp.bfloat16:
+    return jnp.bfloat16
+  else:
+    return jnp.float32