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johndoe6345789
83f1533bce
Extracted from modular repo and reorganized: Compiler Implementation: - 21 compiler source files (frontend, semantic, IR, codegen, runtime) - 15 comprehensive test files (lexer, parser, type checker, backend, etc.) - 9 compiler usage example programs Architecture (5 phases): - Frontend: Lexer, parser, AST generation (lexer.mojo, parser.mojo, ast.mojo) - Semantic: Type system, checking, symbol resolution (3 files) - IR: MLIR code generation (mlir_gen.mojo, mojo_dialect.mojo) - Codegen: LLVM backend, optimization passes (llvm_backend.mojo, optimizer.mojo) - Runtime: Memory mgmt, reflection, async support (3 files) File Organization: - mojo/compiler/src/: Compiler implementation (21 files, 952K) - mojo/compiler/tests/: Test suite (15 files) - mojo/compiler/examples/: Usage examples (9 files) - mojo/samples/: Mojo language examples (37 files, moved from examples/) Documentation: - mojo/CLAUDE.md: Project-level guide - mojo/compiler/CLAUDE.md: Detailed architecture documentation - mojo/compiler/README.md: Quick start guide - mojo/samples/README.md: Example programs guide Status: - Compiler architecture complete (Phase 4) - Full test coverage included - Ready for continued development and integration Files tracked: - 45 new compiler files (21 src + 15 tests + 9 examples) - 1 moved existing directory (examples → samples) - 3 documentation files created - 1 root CLAUDE.md updated Co-Authored-By: Claude Haiku 4.5 <noreply@anthropic.com>
76 lines
2.7 KiB
Mojo
Executable File
76 lines
2.7 KiB
Mojo
Executable File
# ===----------------------------------------------------------------------=== #
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# Copyright (c) 2025, Modular Inc. All rights reserved.
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#
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# Licensed under the Apache License v2.0 with LLVM Exceptions:
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# https://llvm.org/LICENSE.txt
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ===----------------------------------------------------------------------=== #
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from math import ceildiv
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from sys import has_accelerator
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from gpu import global_idx
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from gpu.host import DeviceContext
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from layout import Layout, LayoutTensor
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comptime float_dtype = DType.float32
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comptime VECTOR_WIDTH = 10
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comptime BLOCK_SIZE = 5
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comptime layout = Layout.row_major(VECTOR_WIDTH)
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def main():
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constrained[has_accelerator(), "This example requires a supported GPU"]()
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# Get context for the attached GPU
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var ctx = DeviceContext()
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# Allocate data on the GPU address space
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var lhs_buffer = ctx.enqueue_create_buffer[float_dtype](VECTOR_WIDTH)
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var rhs_buffer = ctx.enqueue_create_buffer[float_dtype](VECTOR_WIDTH)
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var out_buffer = ctx.enqueue_create_buffer[float_dtype](VECTOR_WIDTH)
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# Fill in values across the entire width
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lhs_buffer.enqueue_fill(1.25)
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rhs_buffer.enqueue_fill(2.5)
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# Wrap the device buffers in tensors
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var lhs_tensor = LayoutTensor[float_dtype, layout](lhs_buffer)
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var rhs_tensor = LayoutTensor[float_dtype, layout](rhs_buffer)
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var out_tensor = LayoutTensor[float_dtype, layout](out_buffer)
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# Calculate the number of blocks needed to cover the vector
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var grid_dim = ceildiv(VECTOR_WIDTH, BLOCK_SIZE)
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# Launch the vector_addition function as a GPU kernel
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ctx.enqueue_function[vector_addition, vector_addition](
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lhs_tensor,
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rhs_tensor,
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out_tensor,
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VECTOR_WIDTH,
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grid_dim=grid_dim,
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block_dim=BLOCK_SIZE,
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)
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# Map to host so that values can be printed from the CPU
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with out_buffer.map_to_host() as host_buffer:
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var host_tensor = LayoutTensor[float_dtype, layout](host_buffer)
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print("Resulting vector:", host_tensor)
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fn vector_addition(
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lhs_tensor: LayoutTensor[float_dtype, layout, MutAnyOrigin],
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rhs_tensor: LayoutTensor[float_dtype, layout, MutAnyOrigin],
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out_tensor: LayoutTensor[float_dtype, layout, MutAnyOrigin],
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size: Int,
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):
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"""The calculation to perform across the vector on the GPU."""
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var global_tid = global_idx.x
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if global_tid < UInt(size):
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out_tensor[global_tid] = lhs_tensor[global_tid] + rhs_tensor[global_tid]
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